Map Syphilis
CardenasA
2023-08-20
Cargando los dataframe
departamentos <- read_sf("LÃmites - mapas/DEPARTAMENTOS_inei_geogpsperu_suyopomalia.shp")
data_ins_long <- read_csv("Data_final/ins_final_long.csv")## Rows: 50646 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): distrito, departamento, provincia, rango_edad
## dbl (3): test_number, tamizaje_reactivo, year
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_ins_wide <- read_csv("Data_final/ins_final_wide.csv")## Rows: 18520 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): distrito, departamento, provincia
## dbl (9): year, test_number_12-17 años, test_number_18-29 años, test_number_3...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.pobfem <- read_csv("Data_final/pobfem_year.csv")## Rows: 325 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): NOMBDEP
## dbl (2): UBIGEO, year
## num (1): POBFEM
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Limpiando la data
data_ins_long = rename(
data_ins_long, c("NOMBDEP"="departamento","NOMBPROV"="provincia","NOMBDIST"="distrito"))
data_ins_wide = rename(
data_ins_wide, c("NOMBDEP"="departamento","NOMBPROV"="provincia","NOMBDIST"="distrito"))Agrupamos la data para tener la suma de tamizajes y tamizajes reactivos por año:
prepplot <- data_ins_long %>%
group_by(NOMBDEP, year) %>%
summarise(ntest =sum(test_number)) ## `summarise()` has grouped output by 'NOMBDEP'. You can override using the
## `.groups` argument.prepplot2 <- data_ins_long %>%
group_by(NOMBDEP, year) %>%
summarise(tamizaje_reactivo =sum(tamizaje_reactivo)) ## `summarise()` has grouped output by 'NOMBDEP'. You can override using the
## `.groups` argument.Unimos dataframes y obtenemos el número de casos ajustado por población
data_total_ntest <- prepplot %>%
left_join(departamentos, by = c("NOMBDEP")) %>%
left_join(pobfem, by = c("NOMBDEP","year")) %>%
mutate(rate = (ntest/POBFEM)*10000)
data_total_reactivos <- prepplot2 %>%
left_join(departamentos, by = c("NOMBDEP")) %>%
left_join(pobfem, by = c("NOMBDEP","year")) %>%
mutate(rate = (tamizaje_reactivo/POBFEM)*10000)rm(prepplot,prepplot2,pobfem,departamentos)MAPAS
Tamizajes de SÃfilis por departamento
ggplot() +
geom_sf(data = data_total_ntest, aes(geometry = geometry,fill = ntest), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, INS data",
fill = "Syphilis screening")
# Tamizajes reactivos de SÃfilis por departamentos
ggplot() +
geom_sf(data = data_total_reactivos, aes(geometry = geometry,fill = tamizaje_reactivo), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Cases of syphilis in pregnants, INS data",
fill = "Syphilis reactive")
#Tamizajes de SÃfilis por departamento, ajustado por población
ggplot() +
geom_sf(data = data_total_ntest, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, INS data",
fill = "Syphilis screening per 10000 hab")
#SÃfilis reactivo por departamento, ajustado por población
ggplot() +
geom_sf(data = data_total_reactivos, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Cases of syphilis in pregnants, INS data",
fill = "Syphilis in pregnants per 10000 hab.")
## Tamizaje por departamento, todos los años
data_total_ntesttotal <- data_total_ntest %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed (2011 - 2022)"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntesttotal| Syphilis test performed (2011 - 2022) | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2018 | 5155 | 69926 | 737.2079055 |
| HUANCAVELICA | 2018 | 13188 | 187245 | 704.3178723 |
| AMAZONAS | 2018 | 13675 | 203569 | 671.7624000 |
| AMAZONAS | 2017 | 13201 | 201423 | 655.3869220 |
| LA LIBERTAD | 2018 | 61912 | 978126 | 632.9654871 |
| MADRE DE DIOS | 2017 | 4178 | 67082 | 622.8198324 |
| MADRE DE DIOS | 2022 | 4801 | 80963 | 592.9869200 |
| MADRE DE DIOS | 2021 | 4585 | 78293 | 585.6206813 |
| AMAZONAS | 2022 | 12156 | 208363 | 583.4049231 |
| AMAZONAS | 2016 | 11214 | 199474 | 562.1785295 |
| MADRE DE DIOS | 2016 | 3563 | 64381 | 553.4241469 |
| UCAYALI | 2022 | 16239 | 294437 | 551.5271518 |
| MADRE DE DIOS | 2014 | 3099 | 59752 | 518.6437274 |
| HUANCAVELICA | 2017 | 9225 | 190016 | 485.4854328 |
| PASCO | 2018 | 6025 | 131785 | 457.1840498 |
| LORETO | 2018 | 21926 | 483188 | 453.7778256 |
| LORETO | 2021 | 22725 | 501365 | 453.2625931 |
| APURIMAC | 2022 | 9433 | 209664 | 449.9103327 |
| PIURA | 2018 | 44022 | 984282 | 447.2498735 |
| HUANUCO | 2017 | 16585 | 373161 | 444.4462310 |
| LORETO | 2022 | 22360 | 505412 | 442.4113397 |
| LORETO | 2017 | 20906 | 475588 | 439.5821593 |
| AMAZONAS | 2015 | 8675 | 198081 | 437.9521509 |
| CUSCO | 2018 | 28558 | 653351 | 437.1004253 |
| HUANUCO | 2022 | 16195 | 373411 | 433.7044168 |
| HUANUCO | 2018 | 16008 | 374602 | 427.3335433 |
| APURIMAC | 2021 | 8903 | 210201 | 423.5469860 |
| PASCO | 2021 | 5538 | 131029 | 422.6545269 |
| MADRE DE DIOS | 2015 | 2574 | 61928 | 415.6439736 |
| HUANUCO | 2016 | 15397 | 372089 | 413.7988492 |
| LA LIBERTAD | 2017 | 39198 | 957196 | 409.5086064 |
| AMAZONAS | 2014 | 8059 | 197364 | 408.3318133 |
| HUANUCO | 2021 | 15241 | 374996 | 406.4310019 |
| PASCO | 2022 | 5273 | 130170 | 405.0856572 |
| LA LIBERTAD | 2016 | 37584 | 937293 | 400.9845374 |
| JUNIN | 2018 | 26330 | 670777 | 392.5298572 |
| PASCO | 2017 | 5159 | 131573 | 392.1017230 |
| TUMBES | 2018 | 4355 | 111218 | 391.5733065 |
| MADRE DE DIOS | 2020 | 2956 | 75596 | 391.0259802 |
| UCAYALI | 2021 | 11171 | 288087 | 387.7648072 |
| UCAYALI | 2018 | 10219 | 266990 | 382.7484175 |
| MADRE DE DIOS | 2013 | 2164 | 57780 | 374.5240568 |
| SAN MARTIN | 2021 | 16046 | 432026 | 371.4128316 |
| APURIMAC | 2018 | 7739 | 208910 | 370.4466038 |
| AMAZONAS | 2021 | 7689 | 207863 | 369.9071023 |
| LA LIBERTAD | 2014 | 33342 | 905654 | 368.1538424 |
| HUANUCO | 2015 | 13528 | 372053 | 363.6041102 |
| CAJAMARCA | 2021 | 26296 | 727255 | 361.5788135 |
| PASCO | 2020 | 4708 | 131652 | 357.6094552 |
| SAN MARTIN | 2022 | 15595 | 438293 | 355.8122078 |
| LA LIBERTAD | 2013 | 31490 | 893226 | 352.5423577 |
| HUANUCO | 2020 | 13179 | 375922 | 350.5780454 |
| ICA | 2018 | 15949 | 461287 | 345.7500428 |
| LORETO | 2016 | 16174 | 468466 | 345.2545115 |
| TACNA | 2018 | 6053 | 175677 | 344.5527872 |
| APURIMAC | 2020 | 7224 | 210366 | 343.4015002 |
| AREQUIPA | 2018 | 25013 | 728576 | 343.3135322 |
| AMAZONAS | 2013 | 6745 | 197083 | 342.2415936 |
| CAJAMARCA | 2022 | 24832 | 726838 | 341.6442178 |
| APURIMAC | 2016 | 7028 | 206775 | 339.8863499 |
| UCAYALI | 2017 | 8806 | 259475 | 339.3775894 |
| APURIMAC | 2017 | 7015 | 207742 | 337.6784666 |
| LA LIBERTAD | 2015 | 30908 | 919993 | 335.9590779 |
| CUSCO | 2017 | 21522 | 643519 | 334.4423397 |
| SAN MARTIN | 2018 | 13571 | 408581 | 332.1495615 |
| CUSCO | 2014 | 20613 | 621301 | 331.7715568 |
| HUANCAVELICA | 2022 | 5700 | 173606 | 328.3296660 |
| HUANCAVELICA | 2021 | 5781 | 177521 | 325.6516130 |
| PIURA | 2017 | 31477 | 966689 | 325.6166151 |
| LAMBAYEQUE | 2017 | 20834 | 641219 | 324.9123934 |
| JUNIN | 2017 | 21509 | 663430 | 324.2090349 |
| LAMBAYEQUE | 2018 | 21112 | 652399 | 323.6056462 |
| LAMBAYEQUE | 2016 | 20204 | 630665 | 320.3602547 |
| CUSCO | 2016 | 20278 | 634312 | 319.6849500 |
| JUNIN | 2015 | 20804 | 651410 | 319.3687539 |
| JUNIN | 2022 | 21896 | 689199 | 317.7021441 |
| PIURA | 2016 | 30159 | 949910 | 317.4932362 |
| MOQUEGUA | 2016 | 2682 | 84642 | 316.8639682 |
| AREQUIPA | 2016 | 21994 | 694265 | 316.7954599 |
| LORETO | 2020 | 15683 | 496559 | 315.8335666 |
| CUSCO | 2015 | 19746 | 626826 | 315.0156503 |
| CAJAMARCA | 2018 | 22393 | 718945 | 311.4702794 |
| JUNIN | 2016 | 20436 | 656630 | 311.2254999 |
| MOQUEGUA | 2014 | 2522 | 82507 | 305.6710340 |
| CAJAMARCA | 2020 | 22168 | 726446 | 305.1568871 |
| AREQUIPA | 2017 | 21600 | 711041 | 303.7799508 |
| AREQUIPA | 2015 | 20514 | 679297 | 301.9886736 |
| SAN MARTIN | 2016 | 11691 | 391051 | 298.9635623 |
| JUNIN | 2014 | 19335 | 648069 | 298.3478611 |
| SAN MARTIN | 2017 | 11899 | 399596 | 297.7757535 |
| ANCASH | 2018 | 17043 | 574828 | 296.4886888 |
| SAN MARTIN | 2015 | 11348 | 383619 | 295.8143366 |
| ICA | 2016 | 12844 | 435873 | 294.6729896 |
| CAJAMARCA | 2017 | 20887 | 713729 | 292.6460884 |
| UCAYALI | 2016 | 7283 | 252303 | 288.6608562 |
| APURIMAC | 2015 | 5937 | 206383 | 287.6690425 |
| AREQUIPA | 2014 | 18919 | 666305 | 283.9390369 |
| LAMBAYEQUE | 2021 | 19186 | 680339 | 282.0064703 |
| SAN MARTIN | 2020 | 11973 | 425190 | 281.5917590 |
| JUNIN | 2021 | 19278 | 686597 | 280.7760593 |
| CAJAMARCA | 2015 | 19744 | 706496 | 279.4637195 |
| JUNIN | 2013 | 18026 | 645918 | 279.0756721 |
| PASCO | 2016 | 3664 | 131485 | 278.6629654 |
| CAJAMARCA | 2016 | 19722 | 709170 | 278.0997504 |
| ANCASH | 2022 | 16338 | 591791 | 276.0771962 |
| HUANCAVELICA | 2020 | 4994 | 181196 | 275.6131482 |
| TACNA | 2017 | 4726 | 171904 | 274.9208861 |
| TACNA | 2016 | 4625 | 168246 | 274.8950941 |
| PUNO | 2018 | 17200 | 625906 | 274.8016475 |
| PIURA | 2015 | 25507 | 935175 | 272.7510894 |
| ICA | 2017 | 12216 | 448283 | 272.5064301 |
| ANCASH | 2016 | 15264 | 562270 | 271.4710015 |
| ANCASH | 2017 | 15337 | 568305 | 269.8726916 |
| SAN MARTIN | 2014 | 10134 | 377465 | 268.4752229 |
| TUMBES | 2022 | 3194 | 119646 | 266.9541815 |
| LAMBAYEQUE | 2015 | 16562 | 621762 | 266.3720202 |
| ICA | 2014 | 11010 | 414943 | 265.3376488 |
| ICA | 2015 | 11265 | 424740 | 265.2210764 |
| CALLAO | 2018 | 14704 | 554432 | 265.2083574 |
| TUMBES | 2017 | 2883 | 108747 | 265.1107617 |
| ANCASH | 2021 | 15327 | 589223 | 260.1222288 |
| LAMBAYEQUE | 2022 | 17852 | 687215 | 259.7731423 |
| PIURA | 2022 | 26992 | 1043380 | 258.6976940 |
| TUMBES | 2021 | 3047 | 117811 | 258.6345927 |
| MOQUEGUA | 2013 | 2101 | 81638 | 257.3556432 |
| MOQUEGUA | 2017 | 2205 | 85953 | 256.5355485 |
| CAJAMARCA | 2014 | 18021 | 706065 | 255.2314589 |
| APURIMAC | 2014 | 5272 | 206684 | 255.0753808 |
| SAN MARTIN | 2013 | 9298 | 372145 | 249.8488492 |
| MOQUEGUA | 2015 | 2059 | 83488 | 246.6222691 |
| AYACUCHO | 2018 | 7922 | 322938 | 245.3102453 |
| CALLAO | 2017 | 13170 | 541179 | 243.3575582 |
| MOQUEGUA | 2011 | 1915 | 80126 | 238.9985772 |
| PUNO | 2017 | 14789 | 624277 | 236.8980437 |
| HUANCAVELICA | 2016 | 4563 | 193049 | 236.3648607 |
| CUSCO | 2022 | 16047 | 681600 | 235.4313380 |
| CUSCO | 2021 | 15815 | 676583 | 233.7481137 |
| MOQUEGUA | 2018 | 2008 | 87325 | 229.9456055 |
| PUNO | 2016 | 14312 | 623167 | 229.6655632 |
| TACNA | 2015 | 3784 | 164889 | 229.4877160 |
| ICA | 2022 | 11574 | 508445 | 227.6352408 |
| PUNO | 2022 | 13875 | 620188 | 223.7224841 |
| HUANUCO | 2014 | 8260 | 373348 | 221.2413084 |
| ICA | 2021 | 10925 | 497608 | 219.5503288 |
| AREQUIPA | 2022 | 17316 | 789700 | 219.2731417 |
| CUSCO | 2013 | 13521 | 617011 | 219.1370980 |
| AREQUIPA | 2013 | 14246 | 654590 | 217.6324111 |
| LA LIBERTAD | 2022 | 22744 | 1047119 | 217.2054943 |
| CALLAO | 2016 | 11231 | 528445 | 212.5292131 |
| AYACUCHO | 2022 | 6831 | 326552 | 209.1856733 |
| LAMBAYEQUE | 2020 | 14065 | 672557 | 209.1272561 |
| CAJAMARCA | 2013 | 14373 | 707062 | 203.2777889 |
| HUANUCO | 2013 | 7632 | 375531 | 203.2322232 |
| PUNO | 2021 | 12661 | 623879 | 202.9399932 |
| LIMA | 2018 | 106548 | 5284576 | 201.6207166 |
| PIURA | 2021 | 20521 | 1030681 | 199.1013708 |
| AYACUCHO | 2021 | 6495 | 326668 | 198.8257191 |
| ANCASH | 2015 | 10880 | 557603 | 195.1209014 |
| AMAZONAS | 2012 | 3788 | 197058 | 192.2276690 |
| TUMBES | 2020 | 2195 | 115846 | 189.4756832 |
| PIURA | 2014 | 17466 | 922689 | 189.2945510 |
| PUNO | 2015 | 11752 | 623663 | 188.4351004 |
| ICA | 2020 | 9005 | 486346 | 185.1562468 |
| UCAYALI | 2015 | 4506 | 245857 | 183.2772709 |
| ICA | 2013 | 7387 | 406028 | 181.9332657 |
| CALLAO | 2020 | 10481 | 579808 | 180.7667366 |
| CUSCO | 2020 | 12116 | 670532 | 180.6923458 |
| TUMBES | 2016 | 1894 | 106390 | 178.0242504 |
| ANCASH | 2020 | 10414 | 585806 | 177.7721635 |
| CALLAO | 2022 | 10541 | 602039 | 175.0883248 |
| CALLAO | 2021 | 10328 | 591161 | 174.7070595 |
| TACNA | 2020 | 3162 | 182822 | 172.9551148 |
| SAN MARTIN | 2012 | 6196 | 367403 | 168.6431521 |
| LIMA | 2017 | 86835 | 5165717 | 168.0986395 |
| TACNA | 2021 | 3069 | 185975 | 165.0221804 |
| TACNA | 2022 | 3115 | 188961 | 164.8488312 |
| AYACUCHO | 2017 | 5253 | 320653 | 163.8219508 |
| AYACUCHO | 2016 | 5203 | 318655 | 163.2800364 |
| LA LIBERTAD | 2012 | 14386 | 882171 | 163.0749594 |
| MOQUEGUA | 2022 | 1438 | 91986 | 156.3281369 |
| AYACUCHO | 2020 | 5027 | 326262 | 154.0786239 |
| LIMA | 2016 | 76155 | 5051764 | 150.7493224 |
| PUNO | 2020 | 9291 | 626381 | 148.3282539 |
| TACNA | 2014 | 2337 | 161839 | 144.4027707 |
| PASCO | 2015 | 1902 | 131747 | 144.3676137 |
| PIURA | 2013 | 13014 | 911633 | 142.7548147 |
| LIMA | 2022 | 80394 | 5698013 | 141.0912892 |
| LA LIBERTAD | 2021 | 14025 | 1032621 | 135.8194342 |
| LORETO | 2015 | 6041 | 462639 | 130.5769725 |
| JUNIN | 2020 | 8796 | 682973 | 128.7898643 |
| AMAZONAS | 2020 | 2662 | 207005 | 128.5959276 |
| ANCASH | 2014 | 6980 | 554473 | 125.8853001 |
| PIURA | 2020 | 12749 | 1016979 | 125.3614873 |
| LIMA | 2015 | 62023 | 4949734 | 125.3057235 |
| AREQUIPA | 2021 | 9693 | 776125 | 124.8896763 |
| LIMA | 2021 | 69214 | 5606249 | 123.4586619 |
| TUMBES | 2019 | 1376 | 113640 | 121.0841253 |
| PUNO | 2014 | 7311 | 626183 | 116.7550061 |
| HUANCAVELICA | 2011 | 2421 | 216879 | 111.6290651 |
| TACNA | 2013 | 1762 | 158971 | 110.8378258 |
| APURIMAC | 2013 | 2246 | 207430 | 108.2774912 |
| MOQUEGUA | 2021 | 947 | 90971 | 104.0991085 |
| PASCO | 2014 | 1326 | 132464 | 100.1026694 |
| AYACUCHO | 2015 | 3170 | 317473 | 99.8510110 |
| HUANCAVELICA | 2015 | 1954 | 196670 | 99.3542482 |
| LORETO | 2019 | 4868 | 490451 | 99.2555831 |
| TUMBES | 2015 | 1026 | 104311 | 98.3597128 |
| LIMA | 2014 | 47328 | 4860982 | 97.3630431 |
| JUNIN | 2012 | 6222 | 644512 | 96.5381560 |
| LORETO | 2014 | 4368 | 458340 | 95.3004320 |
| CALLAO | 2019 | 5379 | 567532 | 94.7787966 |
| CALLAO | 2015 | 4864 | 516902 | 94.0990749 |
| MADRE DE DIOS | 2012 | 525 | 55971 | 93.7985743 |
| HUANUCO | 2019 | 3419 | 375743 | 90.9930458 |
| PASCO | 2019 | 1168 | 131887 | 88.5606618 |
| LA LIBERTAD | 2020 | 8995 | 1016769 | 88.4665052 |
| UCAYALI | 2020 | 2482 | 281514 | 88.1661303 |
| PASCO | 2013 | 1168 | 133485 | 87.5004682 |
| TACNA | 2012 | 1276 | 156276 | 81.6504134 |
| LAMBAYEQUE | 2014 | 5009 | 614728 | 81.4831926 |
| SAN MARTIN | 2011 | 2916 | 362987 | 80.3334555 |
| LIMA | 2020 | 44044 | 5508910 | 79.9504802 |
| HUANCAVELICA | 2014 | 1597 | 201161 | 79.3891460 |
| APURIMAC | 2019 | 1660 | 209909 | 79.0818879 |
| AREQUIPA | 2020 | 5973 | 761731 | 78.4135082 |
| CAJAMARCA | 2012 | 5420 | 708952 | 76.4508740 |
| ANCASH | 2013 | 4022 | 552295 | 72.8234005 |
| PUNO | 2013 | 4583 | 630004 | 72.7455699 |
| MOQUEGUA | 2012 | 581 | 80853 | 71.8588055 |
| LORETO | 2013 | 3232 | 455024 | 71.0292204 |
| LAMBAYEQUE | 2019 | 4647 | 663186 | 70.0708399 |
| MOQUEGUA | 2020 | 611 | 89885 | 67.9757468 |
| AMAZONAS | 2011 | 1337 | 197113 | 67.8291132 |
| ICA | 2019 | 2995 | 474202 | 63.1587383 |
| TACNA | 2019 | 1132 | 179379 | 63.1066067 |
| HUANCAVELICA | 2012 | 1331 | 211683 | 62.8770378 |
| CALLAO | 2014 | 3000 | 506714 | 59.2049953 |
| PIURA | 2012 | 4958 | 901697 | 54.9852112 |
| UCAYALI | 2014 | 1314 | 240219 | 54.7000862 |
| HUANCAVELICA | 2013 | 1113 | 206304 | 53.9495114 |
| MADRE DE DIOS | 2019 | 375 | 72801 | 51.5102815 |
| AMAZONAS | 2019 | 1037 | 205550 | 50.4500122 |
| LIMA | 2013 | 23685 | 4780815 | 49.5417622 |
| CALLAO | 2013 | 2103 | 497432 | 42.2771354 |
| PIURA | 2019 | 4059 | 1001455 | 40.5310274 |
| LAMBAYEQUE | 2013 | 2381 | 608884 | 39.1043286 |
| AREQUIPA | 2011 | 2457 | 633964 | 38.7561439 |
| ANCASH | 2019 | 2220 | 580954 | 38.2130083 |
| AREQUIPA | 2012 | 2451 | 643896 | 38.0651534 |
| AYACUCHO | 2014 | 1136 | 317345 | 35.7970033 |
| TUMBES | 2013 | 342 | 100943 | 33.8805068 |
| HUANUCO | 2012 | 1158 | 378160 | 30.6219590 |
| CALLAO | 2012 | 1365 | 488810 | 27.9249606 |
| PUNO | 2019 | 1733 | 626969 | 27.6409200 |
| PUNO | 2012 | 1726 | 634504 | 27.2023502 |
| LA LIBERTAD | 2019 | 2671 | 998509 | 26.7498841 |
| AREQUIPA | 2019 | 1942 | 745822 | 26.0383845 |
| ICA | 2012 | 978 | 397901 | 24.5789782 |
| ANCASH | 2012 | 1274 | 550814 | 23.1294048 |
| TUMBES | 2012 | 219 | 99513 | 22.0071749 |
| LIMA | 2019 | 11509 | 5401318 | 21.3077623 |
| TUMBES | 2011 | 209 | 98207 | 21.2815787 |
| LAMBAYEQUE | 2012 | 1230 | 603897 | 20.3677117 |
| LA LIBERTAD | 2011 | 1761 | 871946 | 20.1962048 |
| MOQUEGUA | 2019 | 173 | 88666 | 19.5114249 |
| LIMA | 2012 | 9118 | 4707203 | 19.3703140 |
| CALLAO | 2011 | 922 | 480597 | 19.1844726 |
| PUNO | 2011 | 1210 | 639058 | 18.9341187 |
| TUMBES | 2014 | 186 | 102532 | 18.1406780 |
| UCAYALI | 2012 | 403 | 230473 | 17.4857792 |
| AYACUCHO | 2019 | 539 | 324984 | 16.5854319 |
| CUSCO | 2012 | 943 | 613591 | 15.3685435 |
| UCAYALI | 2011 | 328 | 226123 | 14.5053798 |
| TACNA | 2011 | 208 | 153747 | 13.5287193 |
| JUNIN | 2019 | 792 | 677635 | 11.6877080 |
| UCAYALI | 2013 | 237 | 235131 | 10.0794876 |
| AYACUCHO | 2013 | 300 | 317917 | 9.4364252 |
| UCAYALI | 2019 | 205 | 274464 | 7.4691034 |
| PIURA | 2011 | 592 | 892571 | 6.6325256 |
| AYACUCHO | 2012 | 211 | 318837 | 6.6178016 |
| PASCO | 2012 | 81 | 134653 | 6.0154620 |
| LIMA | 2011 | 2723 | 4638106 | 5.8709309 |
| APURIMAC | 2012 | 112 | 208444 | 5.3731458 |
| HUANCAVELICA | 2019 | 83 | 184413 | 4.5007673 |
| CUSCO | 2019 | 297 | 662719 | 4.4815374 |
| LORETO | 2011 | 179 | 449948 | 3.9782375 |
| JUNIN | 2011 | 216 | 643407 | 3.3571285 |
| CAJAMARCA | 2011 | 221 | 711198 | 3.1074328 |
| MADRE DE DIOS | 2011 | 13 | 54288 | 2.3946360 |
| HUANUCO | 2011 | 85 | 380794 | 2.2321780 |
| PASCO | 2011 | 21 | 135812 | 1.5462551 |
| APURIMAC | 2011 | 29 | 209550 | 1.3839179 |
| LORETO | 2012 | 59 | 452343 | 1.3043200 |
| ICA | 2011 | 50 | 390469 | 1.2805114 |
| SAN MARTIN | 2019 | 43 | 417336 | 1.0303449 |
| AYACUCHO | 2011 | 26 | 319750 | 0.8131353 |
| CAJAMARCA | 2019 | 45 | 723592 | 0.6218974 |
| ANCASH | 2011 | 21 | 549780 | 0.3819710 |
| LAMBAYEQUE | 2011 | 21 | 599438 | 0.3503281 |
| CUSCO | 2011 | 7 | 610678 | 0.1146267 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntesttotal)TAMIZAJES POR AÑO
2022
data_total_ntest22 <- data_total_ntest %>%
filter(year == "2022")
ggplot() +
geom_sf(data = data_total_ntest22, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2022",
fill = "Syphilis screening per 10000 hab")
data_total_ntest22 <- data_total_ntest22 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2022"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest22| Syphilis test performed in 2022 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2022 | 4801 | 80963 | 592.9869 |
| AMAZONAS | 2022 | 12156 | 208363 | 583.4049 |
| UCAYALI | 2022 | 16239 | 294437 | 551.5272 |
| APURIMAC | 2022 | 9433 | 209664 | 449.9103 |
| LORETO | 2022 | 22360 | 505412 | 442.4113 |
| HUANUCO | 2022 | 16195 | 373411 | 433.7044 |
| PASCO | 2022 | 5273 | 130170 | 405.0857 |
| SAN MARTIN | 2022 | 15595 | 438293 | 355.8122 |
| CAJAMARCA | 2022 | 24832 | 726838 | 341.6442 |
| HUANCAVELICA | 2022 | 5700 | 173606 | 328.3297 |
| JUNIN | 2022 | 21896 | 689199 | 317.7021 |
| ANCASH | 2022 | 16338 | 591791 | 276.0772 |
| TUMBES | 2022 | 3194 | 119646 | 266.9542 |
| LAMBAYEQUE | 2022 | 17852 | 687215 | 259.7731 |
| PIURA | 2022 | 26992 | 1043380 | 258.6977 |
| CUSCO | 2022 | 16047 | 681600 | 235.4313 |
| ICA | 2022 | 11574 | 508445 | 227.6352 |
| PUNO | 2022 | 13875 | 620188 | 223.7225 |
| AREQUIPA | 2022 | 17316 | 789700 | 219.2731 |
| LA LIBERTAD | 2022 | 22744 | 1047119 | 217.2055 |
| AYACUCHO | 2022 | 6831 | 326552 | 209.1857 |
| CALLAO | 2022 | 10541 | 602039 | 175.0883 |
| TACNA | 2022 | 3115 | 188961 | 164.8488 |
| MOQUEGUA | 2022 | 1438 | 91986 | 156.3281 |
| LIMA | 2022 | 80394 | 5698013 | 141.0913 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest22)2021
data_total_ntest21 <- data_total_ntest %>%
filter(year == "2021")
ggplot() +
geom_sf(data = data_total_ntest21, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2021",
fill = "Syphilis screening per 10000 hab")
data_total_ntest21 <- data_total_ntest21 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2021"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest21| Syphilis test performed in 2021 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2021 | 4585 | 78293 | 585.6207 |
| LORETO | 2021 | 22725 | 501365 | 453.2626 |
| APURIMAC | 2021 | 8903 | 210201 | 423.5470 |
| PASCO | 2021 | 5538 | 131029 | 422.6545 |
| HUANUCO | 2021 | 15241 | 374996 | 406.4310 |
| UCAYALI | 2021 | 11171 | 288087 | 387.7648 |
| SAN MARTIN | 2021 | 16046 | 432026 | 371.4128 |
| AMAZONAS | 2021 | 7689 | 207863 | 369.9071 |
| CAJAMARCA | 2021 | 26296 | 727255 | 361.5788 |
| HUANCAVELICA | 2021 | 5781 | 177521 | 325.6516 |
| LAMBAYEQUE | 2021 | 19186 | 680339 | 282.0065 |
| JUNIN | 2021 | 19278 | 686597 | 280.7761 |
| ANCASH | 2021 | 15327 | 589223 | 260.1222 |
| TUMBES | 2021 | 3047 | 117811 | 258.6346 |
| CUSCO | 2021 | 15815 | 676583 | 233.7481 |
| ICA | 2021 | 10925 | 497608 | 219.5503 |
| PUNO | 2021 | 12661 | 623879 | 202.9400 |
| PIURA | 2021 | 20521 | 1030681 | 199.1014 |
| AYACUCHO | 2021 | 6495 | 326668 | 198.8257 |
| CALLAO | 2021 | 10328 | 591161 | 174.7071 |
| TACNA | 2021 | 3069 | 185975 | 165.0222 |
| LA LIBERTAD | 2021 | 14025 | 1032621 | 135.8194 |
| AREQUIPA | 2021 | 9693 | 776125 | 124.8897 |
| LIMA | 2021 | 69214 | 5606249 | 123.4587 |
| MOQUEGUA | 2021 | 947 | 90971 | 104.0991 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest21)2020
data_total_ntest20 <- data_total_ntest %>%
filter(year == "2020")
ggplot() +
geom_sf(data = data_total_ntest20, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2020",
fill = "Syphilis screening per 10000 hab")
data_total_ntest20 <- data_total_ntest20 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2020"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest20| Syphilis test performed in 2020 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2020 | 2956 | 75596 | 391.02598 |
| PASCO | 2020 | 4708 | 131652 | 357.60946 |
| HUANUCO | 2020 | 13179 | 375922 | 350.57805 |
| APURIMAC | 2020 | 7224 | 210366 | 343.40150 |
| LORETO | 2020 | 15683 | 496559 | 315.83357 |
| CAJAMARCA | 2020 | 22168 | 726446 | 305.15689 |
| SAN MARTIN | 2020 | 11973 | 425190 | 281.59176 |
| HUANCAVELICA | 2020 | 4994 | 181196 | 275.61315 |
| LAMBAYEQUE | 2020 | 14065 | 672557 | 209.12726 |
| TUMBES | 2020 | 2195 | 115846 | 189.47568 |
| ICA | 2020 | 9005 | 486346 | 185.15625 |
| CALLAO | 2020 | 10481 | 579808 | 180.76674 |
| CUSCO | 2020 | 12116 | 670532 | 180.69235 |
| ANCASH | 2020 | 10414 | 585806 | 177.77216 |
| TACNA | 2020 | 3162 | 182822 | 172.95511 |
| AYACUCHO | 2020 | 5027 | 326262 | 154.07862 |
| PUNO | 2020 | 9291 | 626381 | 148.32825 |
| JUNIN | 2020 | 8796 | 682973 | 128.78986 |
| AMAZONAS | 2020 | 2662 | 207005 | 128.59593 |
| PIURA | 2020 | 12749 | 1016979 | 125.36149 |
| LA LIBERTAD | 2020 | 8995 | 1016769 | 88.46651 |
| UCAYALI | 2020 | 2482 | 281514 | 88.16613 |
| LIMA | 2020 | 44044 | 5508910 | 79.95048 |
| AREQUIPA | 2020 | 5973 | 761731 | 78.41351 |
| MOQUEGUA | 2020 | 611 | 89885 | 67.97575 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest20)2019
data_total_ntest19 <- data_total_ntest %>%
filter(year == "2019")
ggplot() +
geom_sf(data = data_total_ntest19, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2019",
fill = "Syphilis screening per 10000 hab")
data_total_ntest19 <- data_total_ntest19 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2019"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest19| Syphilis test performed in 2019 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| TUMBES | 2019 | 1376 | 113640 | 121.0841253 |
| LORETO | 2019 | 4868 | 490451 | 99.2555831 |
| CALLAO | 2019 | 5379 | 567532 | 94.7787966 |
| HUANUCO | 2019 | 3419 | 375743 | 90.9930458 |
| PASCO | 2019 | 1168 | 131887 | 88.5606618 |
| APURIMAC | 2019 | 1660 | 209909 | 79.0818879 |
| LAMBAYEQUE | 2019 | 4647 | 663186 | 70.0708399 |
| ICA | 2019 | 2995 | 474202 | 63.1587383 |
| TACNA | 2019 | 1132 | 179379 | 63.1066067 |
| MADRE DE DIOS | 2019 | 375 | 72801 | 51.5102815 |
| AMAZONAS | 2019 | 1037 | 205550 | 50.4500122 |
| PIURA | 2019 | 4059 | 1001455 | 40.5310274 |
| ANCASH | 2019 | 2220 | 580954 | 38.2130083 |
| PUNO | 2019 | 1733 | 626969 | 27.6409200 |
| LA LIBERTAD | 2019 | 2671 | 998509 | 26.7498841 |
| AREQUIPA | 2019 | 1942 | 745822 | 26.0383845 |
| LIMA | 2019 | 11509 | 5401318 | 21.3077623 |
| MOQUEGUA | 2019 | 173 | 88666 | 19.5114249 |
| AYACUCHO | 2019 | 539 | 324984 | 16.5854319 |
| JUNIN | 2019 | 792 | 677635 | 11.6877080 |
| UCAYALI | 2019 | 205 | 274464 | 7.4691034 |
| HUANCAVELICA | 2019 | 83 | 184413 | 4.5007673 |
| CUSCO | 2019 | 297 | 662719 | 4.4815374 |
| SAN MARTIN | 2019 | 43 | 417336 | 1.0303449 |
| CAJAMARCA | 2019 | 45 | 723592 | 0.6218974 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest19)2018
data_total_ntest18 <- data_total_ntest %>%
filter(year == "2018")
ggplot() +
geom_sf(data = data_total_ntest18, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2018",
fill = "Syphilis screening per 10000 hab")
data_total_ntest18 <- data_total_ntest18 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2018"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest18| Syphilis test performed in 2018 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2018 | 5155 | 69926 | 737.2079 |
| HUANCAVELICA | 2018 | 13188 | 187245 | 704.3179 |
| AMAZONAS | 2018 | 13675 | 203569 | 671.7624 |
| LA LIBERTAD | 2018 | 61912 | 978126 | 632.9655 |
| PASCO | 2018 | 6025 | 131785 | 457.1840 |
| LORETO | 2018 | 21926 | 483188 | 453.7778 |
| PIURA | 2018 | 44022 | 984282 | 447.2499 |
| CUSCO | 2018 | 28558 | 653351 | 437.1004 |
| HUANUCO | 2018 | 16008 | 374602 | 427.3335 |
| JUNIN | 2018 | 26330 | 670777 | 392.5299 |
| TUMBES | 2018 | 4355 | 111218 | 391.5733 |
| UCAYALI | 2018 | 10219 | 266990 | 382.7484 |
| APURIMAC | 2018 | 7739 | 208910 | 370.4466 |
| ICA | 2018 | 15949 | 461287 | 345.7500 |
| TACNA | 2018 | 6053 | 175677 | 344.5528 |
| AREQUIPA | 2018 | 25013 | 728576 | 343.3135 |
| SAN MARTIN | 2018 | 13571 | 408581 | 332.1496 |
| LAMBAYEQUE | 2018 | 21112 | 652399 | 323.6056 |
| CAJAMARCA | 2018 | 22393 | 718945 | 311.4703 |
| ANCASH | 2018 | 17043 | 574828 | 296.4887 |
| PUNO | 2018 | 17200 | 625906 | 274.8016 |
| CALLAO | 2018 | 14704 | 554432 | 265.2084 |
| AYACUCHO | 2018 | 7922 | 322938 | 245.3102 |
| MOQUEGUA | 2018 | 2008 | 87325 | 229.9456 |
| LIMA | 2018 | 106548 | 5284576 | 201.6207 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest18)2017
data_total_ntest17 <- data_total_ntest %>%
filter(year == "2017")
ggplot() +
geom_sf(data = data_total_ntest17, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2017",
fill = "Syphilis screening per 10000 hab")
data_total_ntest17 <- data_total_ntest17 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2017"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest17| Syphilis test performed in 2017 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2017 | 13201 | 201423 | 655.3869 |
| MADRE DE DIOS | 2017 | 4178 | 67082 | 622.8198 |
| HUANCAVELICA | 2017 | 9225 | 190016 | 485.4854 |
| HUANUCO | 2017 | 16585 | 373161 | 444.4462 |
| LORETO | 2017 | 20906 | 475588 | 439.5822 |
| LA LIBERTAD | 2017 | 39198 | 957196 | 409.5086 |
| PASCO | 2017 | 5159 | 131573 | 392.1017 |
| UCAYALI | 2017 | 8806 | 259475 | 339.3776 |
| APURIMAC | 2017 | 7015 | 207742 | 337.6785 |
| CUSCO | 2017 | 21522 | 643519 | 334.4423 |
| PIURA | 2017 | 31477 | 966689 | 325.6166 |
| LAMBAYEQUE | 2017 | 20834 | 641219 | 324.9124 |
| JUNIN | 2017 | 21509 | 663430 | 324.2090 |
| AREQUIPA | 2017 | 21600 | 711041 | 303.7800 |
| SAN MARTIN | 2017 | 11899 | 399596 | 297.7758 |
| CAJAMARCA | 2017 | 20887 | 713729 | 292.6461 |
| TACNA | 2017 | 4726 | 171904 | 274.9209 |
| ICA | 2017 | 12216 | 448283 | 272.5064 |
| ANCASH | 2017 | 15337 | 568305 | 269.8727 |
| TUMBES | 2017 | 2883 | 108747 | 265.1108 |
| MOQUEGUA | 2017 | 2205 | 85953 | 256.5355 |
| CALLAO | 2017 | 13170 | 541179 | 243.3576 |
| PUNO | 2017 | 14789 | 624277 | 236.8980 |
| LIMA | 2017 | 86835 | 5165717 | 168.0986 |
| AYACUCHO | 2017 | 5253 | 320653 | 163.8220 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest17)2016
data_total_ntest16 <- data_total_ntest %>%
filter(year == "2016")
ggplot() +
geom_sf(data = data_total_ntest16, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2016",
fill = "Syphilis screening per 10000 hab")
data_total_ntest16 <- data_total_ntest16 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2016"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest16| Syphilis test performed in 2016 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2016 | 11214 | 199474 | 562.1785 |
| MADRE DE DIOS | 2016 | 3563 | 64381 | 553.4241 |
| HUANUCO | 2016 | 15397 | 372089 | 413.7988 |
| LA LIBERTAD | 2016 | 37584 | 937293 | 400.9845 |
| LORETO | 2016 | 16174 | 468466 | 345.2545 |
| APURIMAC | 2016 | 7028 | 206775 | 339.8863 |
| LAMBAYEQUE | 2016 | 20204 | 630665 | 320.3603 |
| CUSCO | 2016 | 20278 | 634312 | 319.6849 |
| PIURA | 2016 | 30159 | 949910 | 317.4932 |
| MOQUEGUA | 2016 | 2682 | 84642 | 316.8640 |
| AREQUIPA | 2016 | 21994 | 694265 | 316.7955 |
| JUNIN | 2016 | 20436 | 656630 | 311.2255 |
| SAN MARTIN | 2016 | 11691 | 391051 | 298.9636 |
| ICA | 2016 | 12844 | 435873 | 294.6730 |
| UCAYALI | 2016 | 7283 | 252303 | 288.6609 |
| PASCO | 2016 | 3664 | 131485 | 278.6630 |
| CAJAMARCA | 2016 | 19722 | 709170 | 278.0998 |
| TACNA | 2016 | 4625 | 168246 | 274.8951 |
| ANCASH | 2016 | 15264 | 562270 | 271.4710 |
| HUANCAVELICA | 2016 | 4563 | 193049 | 236.3649 |
| PUNO | 2016 | 14312 | 623167 | 229.6656 |
| CALLAO | 2016 | 11231 | 528445 | 212.5292 |
| TUMBES | 2016 | 1894 | 106390 | 178.0243 |
| AYACUCHO | 2016 | 5203 | 318655 | 163.2800 |
| LIMA | 2016 | 76155 | 5051764 | 150.7493 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest16)2015
data_total_ntest15 <- data_total_ntest %>%
filter(year == "2015")
ggplot() +
geom_sf(data = data_total_ntest15, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2015",
fill = "Syphilis screening per 10000 hab")
data_total_ntest15 <- data_total_ntest15 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2015"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest15| Syphilis test performed in 2015 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2015 | 8675 | 198081 | 437.95215 |
| MADRE DE DIOS | 2015 | 2574 | 61928 | 415.64397 |
| HUANUCO | 2015 | 13528 | 372053 | 363.60411 |
| LA LIBERTAD | 2015 | 30908 | 919993 | 335.95908 |
| JUNIN | 2015 | 20804 | 651410 | 319.36875 |
| CUSCO | 2015 | 19746 | 626826 | 315.01565 |
| AREQUIPA | 2015 | 20514 | 679297 | 301.98867 |
| SAN MARTIN | 2015 | 11348 | 383619 | 295.81434 |
| APURIMAC | 2015 | 5937 | 206383 | 287.66904 |
| CAJAMARCA | 2015 | 19744 | 706496 | 279.46372 |
| PIURA | 2015 | 25507 | 935175 | 272.75109 |
| LAMBAYEQUE | 2015 | 16562 | 621762 | 266.37202 |
| ICA | 2015 | 11265 | 424740 | 265.22108 |
| MOQUEGUA | 2015 | 2059 | 83488 | 246.62227 |
| TACNA | 2015 | 3784 | 164889 | 229.48772 |
| ANCASH | 2015 | 10880 | 557603 | 195.12090 |
| PUNO | 2015 | 11752 | 623663 | 188.43510 |
| UCAYALI | 2015 | 4506 | 245857 | 183.27727 |
| PASCO | 2015 | 1902 | 131747 | 144.36761 |
| LORETO | 2015 | 6041 | 462639 | 130.57697 |
| LIMA | 2015 | 62023 | 4949734 | 125.30572 |
| AYACUCHO | 2015 | 3170 | 317473 | 99.85101 |
| HUANCAVELICA | 2015 | 1954 | 196670 | 99.35425 |
| TUMBES | 2015 | 1026 | 104311 | 98.35971 |
| CALLAO | 2015 | 4864 | 516902 | 94.09907 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest15)2014
data_total_ntest14 <- data_total_ntest %>%
filter(year == "2014")
ggplot() +
geom_sf(data = data_total_ntest14, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2014",
fill = "Syphilis screening per 10000 hab")
data_total_ntest14 <- data_total_ntest14 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2014"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest14| Syphilis test performed in 2014 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2014 | 3099 | 59752 | 518.64373 |
| AMAZONAS | 2014 | 8059 | 197364 | 408.33181 |
| LA LIBERTAD | 2014 | 33342 | 905654 | 368.15384 |
| CUSCO | 2014 | 20613 | 621301 | 331.77156 |
| MOQUEGUA | 2014 | 2522 | 82507 | 305.67103 |
| JUNIN | 2014 | 19335 | 648069 | 298.34786 |
| AREQUIPA | 2014 | 18919 | 666305 | 283.93904 |
| SAN MARTIN | 2014 | 10134 | 377465 | 268.47522 |
| ICA | 2014 | 11010 | 414943 | 265.33765 |
| CAJAMARCA | 2014 | 18021 | 706065 | 255.23146 |
| APURIMAC | 2014 | 5272 | 206684 | 255.07538 |
| HUANUCO | 2014 | 8260 | 373348 | 221.24131 |
| PIURA | 2014 | 17466 | 922689 | 189.29455 |
| TACNA | 2014 | 2337 | 161839 | 144.40277 |
| ANCASH | 2014 | 6980 | 554473 | 125.88530 |
| PUNO | 2014 | 7311 | 626183 | 116.75501 |
| PASCO | 2014 | 1326 | 132464 | 100.10267 |
| LIMA | 2014 | 47328 | 4860982 | 97.36304 |
| LORETO | 2014 | 4368 | 458340 | 95.30043 |
| LAMBAYEQUE | 2014 | 5009 | 614728 | 81.48319 |
| HUANCAVELICA | 2014 | 1597 | 201161 | 79.38915 |
| CALLAO | 2014 | 3000 | 506714 | 59.20500 |
| UCAYALI | 2014 | 1314 | 240219 | 54.70009 |
| AYACUCHO | 2014 | 1136 | 317345 | 35.79700 |
| TUMBES | 2014 | 186 | 102532 | 18.14068 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest14)2013
data_total_ntest13 <- data_total_ntest %>%
filter(year == "2013")
ggplot() +
geom_sf(data = data_total_ntest13, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2013",
fill = "Syphilis screening per 10000 hab")
data_total_ntest13 <- data_total_ntest13 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2013"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest13| Syphilis test performed in 2013 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2013 | 2164 | 57780 | 374.524057 |
| LA LIBERTAD | 2013 | 31490 | 893226 | 352.542358 |
| AMAZONAS | 2013 | 6745 | 197083 | 342.241594 |
| JUNIN | 2013 | 18026 | 645918 | 279.075672 |
| MOQUEGUA | 2013 | 2101 | 81638 | 257.355643 |
| SAN MARTIN | 2013 | 9298 | 372145 | 249.848849 |
| CUSCO | 2013 | 13521 | 617011 | 219.137098 |
| AREQUIPA | 2013 | 14246 | 654590 | 217.632411 |
| CAJAMARCA | 2013 | 14373 | 707062 | 203.277789 |
| HUANUCO | 2013 | 7632 | 375531 | 203.232223 |
| ICA | 2013 | 7387 | 406028 | 181.933266 |
| PIURA | 2013 | 13014 | 911633 | 142.754815 |
| TACNA | 2013 | 1762 | 158971 | 110.837826 |
| APURIMAC | 2013 | 2246 | 207430 | 108.277491 |
| PASCO | 2013 | 1168 | 133485 | 87.500468 |
| ANCASH | 2013 | 4022 | 552295 | 72.823401 |
| PUNO | 2013 | 4583 | 630004 | 72.745570 |
| LORETO | 2013 | 3232 | 455024 | 71.029220 |
| HUANCAVELICA | 2013 | 1113 | 206304 | 53.949511 |
| LIMA | 2013 | 23685 | 4780815 | 49.541762 |
| CALLAO | 2013 | 2103 | 497432 | 42.277135 |
| LAMBAYEQUE | 2013 | 2381 | 608884 | 39.104329 |
| TUMBES | 2013 | 342 | 100943 | 33.880507 |
| UCAYALI | 2013 | 237 | 235131 | 10.079488 |
| AYACUCHO | 2013 | 300 | 317917 | 9.436425 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest13)2012
data_total_ntest12 <- data_total_ntest %>%
filter(year == "2012")
ggplot() +
geom_sf(data = data_total_ntest12, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2012",
fill = "Syphilis screening per 10000 hab")
data_total_ntest12 <- data_total_ntest12 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2012"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest12| Syphilis test performed in 2012 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2012 | 3788 | 197058 | 192.227669 |
| SAN MARTIN | 2012 | 6196 | 367403 | 168.643152 |
| LA LIBERTAD | 2012 | 14386 | 882171 | 163.074959 |
| JUNIN | 2012 | 6222 | 644512 | 96.538156 |
| MADRE DE DIOS | 2012 | 525 | 55971 | 93.798574 |
| TACNA | 2012 | 1276 | 156276 | 81.650413 |
| CAJAMARCA | 2012 | 5420 | 708952 | 76.450874 |
| MOQUEGUA | 2012 | 581 | 80853 | 71.858805 |
| HUANCAVELICA | 2012 | 1331 | 211683 | 62.877038 |
| PIURA | 2012 | 4958 | 901697 | 54.985211 |
| AREQUIPA | 2012 | 2451 | 643896 | 38.065153 |
| HUANUCO | 2012 | 1158 | 378160 | 30.621959 |
| CALLAO | 2012 | 1365 | 488810 | 27.924961 |
| PUNO | 2012 | 1726 | 634504 | 27.202350 |
| ICA | 2012 | 978 | 397901 | 24.578978 |
| ANCASH | 2012 | 1274 | 550814 | 23.129405 |
| TUMBES | 2012 | 219 | 99513 | 22.007175 |
| LAMBAYEQUE | 2012 | 1230 | 603897 | 20.367712 |
| LIMA | 2012 | 9118 | 4707203 | 19.370314 |
| UCAYALI | 2012 | 403 | 230473 | 17.485779 |
| CUSCO | 2012 | 943 | 613591 | 15.368544 |
| AYACUCHO | 2012 | 211 | 318837 | 6.617802 |
| PASCO | 2012 | 81 | 134653 | 6.015462 |
| APURIMAC | 2012 | 112 | 208444 | 5.373146 |
| LORETO | 2012 | 59 | 452343 | 1.304320 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest12)2011
data_total_ntest11 <- data_total_ntest %>%
filter(year == "2011")
ggplot() +
geom_sf(data = data_total_ntest11, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis screening in pregnants, 2011",
fill = "Syphilis screening per 10000 hab")
data_total_ntest11 <- data_total_ntest11 %>%
select(NOMBDEP, year, ntest, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Syphilis test performed in 2011"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_ntest11| Syphilis test performed in 2011 | ||||
| NOMBDEP | year | ntest | POBFEM1 | rate2 |
|---|---|---|---|---|
| MOQUEGUA | 2011 | 1915 | 80126 | 238.9985772 |
| HUANCAVELICA | 2011 | 2421 | 216879 | 111.6290651 |
| SAN MARTIN | 2011 | 2916 | 362987 | 80.3334555 |
| AMAZONAS | 2011 | 1337 | 197113 | 67.8291132 |
| AREQUIPA | 2011 | 2457 | 633964 | 38.7561439 |
| TUMBES | 2011 | 209 | 98207 | 21.2815787 |
| LA LIBERTAD | 2011 | 1761 | 871946 | 20.1962048 |
| CALLAO | 2011 | 922 | 480597 | 19.1844726 |
| PUNO | 2011 | 1210 | 639058 | 18.9341187 |
| UCAYALI | 2011 | 328 | 226123 | 14.5053798 |
| TACNA | 2011 | 208 | 153747 | 13.5287193 |
| PIURA | 2011 | 592 | 892571 | 6.6325256 |
| LIMA | 2011 | 2723 | 4638106 | 5.8709309 |
| LORETO | 2011 | 179 | 449948 | 3.9782375 |
| JUNIN | 2011 | 216 | 643407 | 3.3571285 |
| CAJAMARCA | 2011 | 221 | 711198 | 3.1074328 |
| MADRE DE DIOS | 2011 | 13 | 54288 | 2.3946360 |
| HUANUCO | 2011 | 85 | 380794 | 2.2321780 |
| PASCO | 2011 | 21 | 135812 | 1.5462551 |
| APURIMAC | 2011 | 29 | 209550 | 1.3839179 |
| ICA | 2011 | 50 | 390469 | 1.2805114 |
| AYACUCHO | 2011 | 26 | 319750 | 0.8131353 |
| ANCASH | 2011 | 21 | 549780 | 0.3819710 |
| LAMBAYEQUE | 2011 | 21 | 599438 | 0.3503281 |
| CUSCO | 2011 | 7 | 610678 | 0.1146267 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_ntest11)Tamizaje reactivo en gestantes por departamento, todos los años
data_reactivos_allyears <- data_total_reactivos %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants (2011 - 2022)"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_reactivos_allyears| Cases of syphilis in pregnants (2011 - 2022) | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2016 | 149 | 199474 | 7.46964517 |
| AMAZONAS | 2013 | 144 | 197083 | 7.30656627 |
| MADRE DE DIOS | 2018 | 51 | 69926 | 7.29342448 |
| AMAZONAS | 2015 | 136 | 198081 | 6.86587810 |
| AMAZONAS | 2018 | 121 | 203569 | 5.94393056 |
| AMAZONAS | 2014 | 111 | 197364 | 5.62412598 |
| MADRE DE DIOS | 2021 | 43 | 78293 | 5.49218960 |
| PASCO | 2018 | 72 | 131785 | 5.46344425 |
| MADRE DE DIOS | 2022 | 44 | 80963 | 5.43458123 |
| AMAZONAS | 2017 | 105 | 201423 | 5.21291014 |
| MADRE DE DIOS | 2017 | 34 | 67082 | 5.06842372 |
| UCAYALI | 2022 | 148 | 294437 | 5.02654218 |
| PASCO | 2017 | 66 | 131573 | 5.01622673 |
| UCAYALI | 2018 | 132 | 266990 | 4.94400539 |
| UCAYALI | 2021 | 119 | 288087 | 4.13069663 |
| AMAZONAS | 2022 | 73 | 208363 | 3.50350110 |
| UCAYALI | 2017 | 88 | 259475 | 3.39146353 |
| PASCO | 2021 | 44 | 131029 | 3.35803524 |
| MADRE DE DIOS | 2016 | 20 | 64381 | 3.10650658 |
| UCAYALI | 2016 | 76 | 252303 | 3.01225114 |
| PASCO | 2022 | 39 | 130170 | 2.99608205 |
| PASCO | 2016 | 39 | 131485 | 2.96611781 |
| LORETO | 2017 | 141 | 475588 | 2.96475100 |
| SAN MARTIN | 2018 | 121 | 408581 | 2.96146908 |
| LORETO | 2018 | 138 | 483188 | 2.85603119 |
| MADRE DE DIOS | 2020 | 21 | 75596 | 2.77792476 |
| MADRE DE DIOS | 2013 | 16 | 57780 | 2.76912426 |
| HUANUCO | 2015 | 102 | 372053 | 2.74154489 |
| PASCO | 2020 | 36 | 131652 | 2.73448182 |
| HUANUCO | 2022 | 100 | 373411 | 2.67801431 |
| MADRE DE DIOS | 2014 | 16 | 59752 | 2.67773464 |
| HUANUCO | 2017 | 98 | 373161 | 2.62621228 |
| HUANUCO | 2013 | 98 | 375531 | 2.60963809 |
| LORETO | 2016 | 121 | 468466 | 2.58289823 |
| HUANUCO | 2018 | 95 | 374602 | 2.53602490 |
| HUANUCO | 2016 | 94 | 372089 | 2.52627732 |
| JUNIN | 2018 | 169 | 670777 | 2.51946623 |
| SAN MARTIN | 2015 | 96 | 383619 | 2.50248293 |
| TACNA | 2018 | 43 | 175677 | 2.44767386 |
| HUANUCO | 2014 | 89 | 373348 | 2.38383492 |
| AMAZONAS | 2012 | 46 | 197058 | 2.33433811 |
| PASCO | 2014 | 30 | 132464 | 2.26476628 |
| PASCO | 2013 | 30 | 133485 | 2.24744353 |
| CUSCO | 2018 | 143 | 653351 | 2.18871633 |
| HUANUCO | 2021 | 82 | 374996 | 2.18668999 |
| ICA | 2022 | 105 | 508445 | 2.06512012 |
| PASCO | 2015 | 27 | 131747 | 2.04938253 |
| LORETO | 2022 | 103 | 505412 | 2.03794132 |
| CALLAO | 2022 | 122 | 602039 | 2.02644679 |
| TACNA | 2021 | 37 | 185975 | 1.98951472 |
| JUNIN | 2022 | 137 | 689199 | 1.98781484 |
| CALLAO | 2020 | 115 | 579808 | 1.98341520 |
| SAN MARTIN | 2016 | 77 | 391051 | 1.96905263 |
| HUANUCO | 2020 | 74 | 375922 | 1.96849346 |
| MADRE DE DIOS | 2015 | 12 | 61928 | 1.93773414 |
| UCAYALI | 2015 | 47 | 245857 | 1.91168037 |
| CALLAO | 2021 | 113 | 591161 | 1.91149281 |
| CALLAO | 2018 | 103 | 554432 | 1.85775713 |
| SAN MARTIN | 2017 | 74 | 399596 | 1.85187039 |
| TUMBES | 2022 | 22 | 119646 | 1.83875767 |
| PUNO | 2018 | 113 | 625906 | 1.80538292 |
| MADRE DE DIOS | 2012 | 10 | 55971 | 1.78663951 |
| ICA | 2021 | 87 | 497608 | 1.74836417 |
| AMAZONAS | 2021 | 36 | 207863 | 1.73190996 |
| LORETO | 2021 | 84 | 501365 | 1.67542609 |
| PASCO | 2019 | 22 | 131887 | 1.66809466 |
| SAN MARTIN | 2022 | 72 | 438293 | 1.64273671 |
| MOQUEGUA | 2022 | 15 | 91986 | 1.63068293 |
| LIMA | 2022 | 905 | 5698013 | 1.58827296 |
| JUNIN | 2021 | 107 | 686597 | 1.55841054 |
| SAN MARTIN | 2014 | 58 | 377465 | 1.53656630 |
| AYACUCHO | 2021 | 50 | 326668 | 1.53060600 |
| SAN MARTIN | 2021 | 65 | 432026 | 1.50453908 |
| LORETO | 2020 | 74 | 496559 | 1.49025594 |
| CALLAO | 2017 | 80 | 541179 | 1.47825396 |
| TUMBES | 2018 | 16 | 111218 | 1.43861605 |
| CUSCO | 2017 | 91 | 643519 | 1.41409966 |
| JUNIN | 2015 | 92 | 651410 | 1.41232097 |
| LA LIBERTAD | 2018 | 136 | 978126 | 1.39041391 |
| AYACUCHO | 2022 | 44 | 326552 | 1.34741174 |
| JUNIN | 2017 | 89 | 663430 | 1.34151305 |
| JUNIN | 2013 | 86 | 645918 | 1.33143836 |
| ICA | 2018 | 61 | 461287 | 1.32238715 |
| TACNA | 2020 | 24 | 182822 | 1.31275229 |
| ICA | 2020 | 63 | 486346 | 1.29537408 |
| UCAYALI | 2014 | 30 | 240219 | 1.24886041 |
| LORETO | 2015 | 56 | 462639 | 1.21044702 |
| MOQUEGUA | 2021 | 11 | 90971 | 1.20917655 |
| APURIMAC | 2022 | 25 | 209664 | 1.19238400 |
| CALLAO | 2019 | 67 | 567532 | 1.18055017 |
| CUSCO | 2016 | 72 | 634312 | 1.13508810 |
| SAN MARTIN | 2013 | 41 | 372145 | 1.10172110 |
| CALLAO | 2016 | 58 | 528445 | 1.09755982 |
| PUNO | 2017 | 68 | 624277 | 1.08926006 |
| AYACUCHO | 2020 | 35 | 326262 | 1.07275748 |
| HUANCAVELICA | 2018 | 20 | 187245 | 1.06811931 |
| MOQUEGUA | 2016 | 9 | 84642 | 1.06330191 |
| PIURA | 2018 | 100 | 984282 | 1.01596900 |
| ANCASH | 2021 | 59 | 589223 | 1.00131869 |
| HUANCAVELICA | 2017 | 19 | 190016 | 0.99991580 |
| LAMBAYEQUE | 2015 | 62 | 621762 | 0.99716612 |
| LAMBAYEQUE | 2016 | 62 | 630665 | 0.98308928 |
| JUNIN | 2016 | 64 | 656630 | 0.97467371 |
| TACNA | 2015 | 16 | 164889 | 0.97034975 |
| MOQUEGUA | 2014 | 8 | 82507 | 0.96961470 |
| LAMBAYEQUE | 2018 | 63 | 652399 | 0.96566672 |
| ANCASH | 2022 | 56 | 591791 | 0.94628002 |
| TUMBES | 2016 | 10 | 106390 | 0.93993796 |
| LIMA | 2018 | 494 | 5284576 | 0.93479590 |
| APURIMAC | 2021 | 19 | 210201 | 0.90389675 |
| MOQUEGUA | 2020 | 8 | 89885 | 0.89002614 |
| TACNA | 2017 | 15 | 171904 | 0.87258004 |
| LIMA | 2021 | 487 | 5606249 | 0.86867351 |
| CUSCO | 2022 | 59 | 681600 | 0.86561033 |
| AMAZONAS | 2011 | 17 | 197113 | 0.86244946 |
| PIURA | 2022 | 89 | 1043380 | 0.85299699 |
| LA LIBERTAD | 2017 | 81 | 957196 | 0.84622167 |
| TUMBES | 2017 | 9 | 108747 | 0.82760904 |
| ANCASH | 2018 | 47 | 574828 | 0.81763588 |
| LA LIBERTAD | 2015 | 75 | 919993 | 0.81522359 |
| JUNIN | 2020 | 55 | 682973 | 0.80530270 |
| JUNIN | 2014 | 51 | 648069 | 0.78695324 |
| ICA | 2017 | 35 | 448283 | 0.78075680 |
| LA LIBERTAD | 2016 | 73 | 937293 | 0.77883863 |
| TACNA | 2016 | 13 | 168246 | 0.77267810 |
| APURIMAC | 2018 | 16 | 208910 | 0.76588004 |
| CUSCO | 2015 | 48 | 626826 | 0.76576275 |
| AYACUCHO | 2018 | 24 | 322938 | 0.74317671 |
| LA LIBERTAD | 2013 | 65 | 893226 | 0.72769937 |
| AMAZONAS | 2020 | 15 | 207005 | 0.72462018 |
| LORETO | 2014 | 33 | 458340 | 0.71998953 |
| MOQUEGUA | 2015 | 6 | 83488 | 0.71866616 |
| LIMA | 2016 | 363 | 5051764 | 0.71856088 |
| LA LIBERTAD | 2022 | 75 | 1047119 | 0.71625097 |
| SAN MARTIN | 2020 | 30 | 425190 | 0.70556692 |
| PIURA | 2021 | 72 | 1030681 | 0.69856726 |
| HUANCAVELICA | 2022 | 12 | 173606 | 0.69122035 |
| AYACUCHO | 2017 | 22 | 320653 | 0.68609993 |
| LIMA | 2017 | 354 | 5165717 | 0.68528725 |
| LAMBAYEQUE | 2022 | 47 | 687215 | 0.68391988 |
| PIURA | 2016 | 64 | 949910 | 0.67374804 |
| AYACUCHO | 2014 | 21 | 317345 | 0.66174038 |
| PIURA | 2017 | 63 | 966689 | 0.65170908 |
| TACNA | 2022 | 12 | 188961 | 0.63505168 |
| AYACUCHO | 2016 | 20 | 318655 | 0.62763804 |
| HUANCAVELICA | 2016 | 12 | 193049 | 0.62160384 |
| APURIMAC | 2020 | 13 | 210366 | 0.61797058 |
| CUSCO | 2013 | 38 | 617011 | 0.61587233 |
| LORETO | 2013 | 28 | 455024 | 0.61535216 |
| MOQUEGUA | 2013 | 5 | 81638 | 0.61245988 |
| AREQUIPA | 2022 | 48 | 789700 | 0.60782576 |
| CAJAMARCA | 2022 | 44 | 726838 | 0.60536186 |
| ICA | 2014 | 25 | 414943 | 0.60249239 |
| AYACUCHO | 2015 | 19 | 317473 | 0.59847609 |
| TUMBES | 2021 | 7 | 117811 | 0.59417202 |
| PUNO | 2016 | 37 | 623167 | 0.59374132 |
| ANCASH | 2016 | 33 | 562270 | 0.58690665 |
| MOQUEGUA | 2017 | 5 | 85953 | 0.58171326 |
| CAJAMARCA | 2021 | 41 | 727255 | 0.56376374 |
| LAMBAYEQUE | 2017 | 36 | 641219 | 0.56143065 |
| ICA | 2016 | 24 | 435873 | 0.55061910 |
| MADRE DE DIOS | 2019 | 4 | 72801 | 0.54944300 |
| LAMBAYEQUE | 2021 | 37 | 680339 | 0.54384652 |
| PIURA | 2015 | 50 | 935175 | 0.53465929 |
| APURIMAC | 2017 | 11 | 207742 | 0.52950294 |
| ANCASH | 2017 | 30 | 568305 | 0.52788555 |
| SAN MARTIN | 2012 | 19 | 367403 | 0.51714330 |
| LA LIBERTAD | 2014 | 46 | 905654 | 0.50792024 |
| CUSCO | 2020 | 34 | 670532 | 0.50706007 |
| CUSCO | 2021 | 34 | 676583 | 0.50252519 |
| HUANCAVELICA | 2020 | 9 | 181196 | 0.49669971 |
| TUMBES | 2015 | 5 | 104311 | 0.47933583 |
| LIMA | 2020 | 264 | 5508910 | 0.47922366 |
| LAMBAYEQUE | 2020 | 32 | 672557 | 0.47579610 |
| ANCASH | 2015 | 26 | 557603 | 0.46628157 |
| MOQUEGUA | 2018 | 4 | 87325 | 0.45805898 |
| LA LIBERTAD | 2021 | 47 | 1032621 | 0.45515247 |
| CUSCO | 2014 | 28 | 621301 | 0.45066723 |
| LORETO | 2019 | 22 | 490451 | 0.44856673 |
| TACNA | 2012 | 7 | 156276 | 0.44792547 |
| TUMBES | 2019 | 5 | 113640 | 0.43998592 |
| CAJAMARCA | 2014 | 31 | 706065 | 0.43905306 |
| TACNA | 2014 | 7 | 161839 | 0.43252862 |
| TUMBES | 2020 | 5 | 115846 | 0.43160748 |
| CAJAMARCA | 2018 | 31 | 718945 | 0.43118736 |
| PUNO | 2021 | 25 | 623879 | 0.40071873 |
| PIURA | 2013 | 36 | 911633 | 0.39489575 |
| HUANCAVELICA | 2021 | 7 | 177521 | 0.39431955 |
| APURIMAC | 2016 | 8 | 206775 | 0.38689397 |
| AREQUIPA | 2021 | 30 | 776125 | 0.38653567 |
| CAJAMARCA | 2020 | 28 | 726446 | 0.38543815 |
| LIMA | 2015 | 188 | 4949734 | 0.37981839 |
| MOQUEGUA | 2012 | 3 | 80853 | 0.37104375 |
| PIURA | 2014 | 33 | 922689 | 0.35765030 |
| AREQUIPA | 2020 | 26 | 761731 | 0.34132784 |
| APURIMAC | 2014 | 7 | 206684 | 0.33868127 |
| TACNA | 2019 | 6 | 179379 | 0.33448731 |
| LIMA | 2014 | 159 | 4860982 | 0.32709440 |
| AREQUIPA | 2017 | 23 | 711041 | 0.32346939 |
| HUANUCO | 2019 | 12 | 375743 | 0.31936723 |
| TACNA | 2013 | 5 | 158971 | 0.31452277 |
| JUNIN | 2012 | 20 | 644512 | 0.31031230 |
| CAJAMARCA | 2017 | 22 | 713729 | 0.30824024 |
| PIURA | 2020 | 31 | 1016979 | 0.30482439 |
| APURIMAC | 2015 | 6 | 206383 | 0.29072162 |
| ANCASH | 2020 | 17 | 585806 | 0.29019846 |
| ANCASH | 2014 | 16 | 554473 | 0.28856229 |
| AREQUIPA | 2018 | 21 | 728576 | 0.28823349 |
| PUNO | 2014 | 17 | 626183 | 0.27148613 |
| ICA | 2013 | 11 | 406028 | 0.27091728 |
| AREQUIPA | 2014 | 18 | 666305 | 0.27014655 |
| LAMBAYEQUE | 2014 | 16 | 614728 | 0.26027772 |
| AREQUIPA | 2016 | 18 | 694265 | 0.25926699 |
| PUNO | 2022 | 16 | 620188 | 0.25798629 |
| LIMA | 2013 | 122 | 4780815 | 0.25518662 |
| CAJAMARCA | 2015 | 17 | 706496 | 0.24062415 |
| PUNO | 2015 | 15 | 623663 | 0.24051451 |
| LA LIBERTAD | 2020 | 24 | 1016769 | 0.23604181 |
| ANCASH | 2013 | 13 | 552295 | 0.23538145 |
| CAJAMARCA | 2016 | 16 | 709170 | 0.22561586 |
| AREQUIPA | 2015 | 15 | 679297 | 0.22081652 |
| CALLAO | 2015 | 11 | 516902 | 0.21280630 |
| PUNO | 2020 | 13 | 626381 | 0.20754142 |
| LA LIBERTAD | 2012 | 18 | 882171 | 0.20404207 |
| HUANCAVELICA | 2015 | 4 | 196670 | 0.20338638 |
| AREQUIPA | 2013 | 13 | 654590 | 0.19859760 |
| LAMBAYEQUE | 2013 | 12 | 608884 | 0.19708187 |
| ICA | 2019 | 9 | 474202 | 0.18979254 |
| HUANCAVELICA | 2012 | 4 | 211683 | 0.18896180 |
| ICA | 2015 | 8 | 424740 | 0.18835052 |
| HUANUCO | 2012 | 7 | 378160 | 0.18510683 |
| LAMBAYEQUE | 2019 | 12 | 663186 | 0.18094471 |
| UCAYALI | 2020 | 5 | 281514 | 0.17761106 |
| UCAYALI | 2013 | 4 | 235131 | 0.17011793 |
| PIURA | 2019 | 16 | 1001455 | 0.15976754 |
| PASCO | 2012 | 2 | 134653 | 0.14852993 |
| HUANCAVELICA | 2013 | 3 | 206304 | 0.14541647 |
| CALLAO | 2013 | 7 | 497432 | 0.14072275 |
| LIMA | 2019 | 76 | 5401318 | 0.14070640 |
| CALLAO | 2014 | 7 | 506714 | 0.13814499 |
| PUNO | 2013 | 8 | 630004 | 0.12698332 |
| AYACUCHO | 2013 | 4 | 317917 | 0.12581900 |
| MOQUEGUA | 2019 | 1 | 88666 | 0.11278280 |
| LA LIBERTAD | 2019 | 11 | 998509 | 0.11016425 |
| TUMBES | 2012 | 1 | 99513 | 0.10048938 |
| LAMBAYEQUE | 2012 | 6 | 603897 | 0.09935469 |
| CAJAMARCA | 2012 | 7 | 708952 | 0.09873729 |
| AMAZONAS | 2019 | 2 | 205550 | 0.09729993 |
| APURIMAC | 2013 | 2 | 207430 | 0.09641807 |
| PUNO | 2019 | 6 | 626969 | 0.09569851 |
| CAJAMARCA | 2013 | 6 | 707062 | 0.08485819 |
| AREQUIPA | 2012 | 5 | 643896 | 0.07765229 |
| ICA | 2012 | 3 | 397901 | 0.07539564 |
| LIMA | 2012 | 34 | 4707203 | 0.07222973 |
| AREQUIPA | 2019 | 5 | 745822 | 0.06704012 |
| CUSCO | 2012 | 4 | 613591 | 0.06519000 |
| CUSCO | 2019 | 4 | 662719 | 0.06035741 |
| PIURA | 2012 | 5 | 901697 | 0.05545100 |
| APURIMAC | 2019 | 1 | 209909 | 0.04763969 |
| LORETO | 2012 | 2 | 452343 | 0.04421424 |
| UCAYALI | 2012 | 1 | 230473 | 0.04338903 |
| UCAYALI | 2019 | 1 | 274464 | 0.03643465 |
| ANCASH | 2012 | 2 | 550814 | 0.03630990 |
| AYACUCHO | 2019 | 1 | 324984 | 0.03077075 |
| CAJAMARCA | 2019 | 2 | 723592 | 0.02763989 |
| SAN MARTIN | 2011 | 1 | 362987 | 0.02754920 |
| CALLAO | 2012 | 1 | 488810 | 0.02045785 |
| ANCASH | 2019 | 1 | 580954 | 0.01721307 |
| AREQUIPA | 2011 | 1 | 633964 | 0.01577377 |
| JUNIN | 2019 | 1 | 677635 | 0.01475721 |
| ANCASH | 2011 | 0 | 549780 | 0.00000000 |
| APURIMAC | 2011 | 0 | 209550 | 0.00000000 |
| APURIMAC | 2012 | 0 | 208444 | 0.00000000 |
| AYACUCHO | 2011 | 0 | 319750 | 0.00000000 |
| AYACUCHO | 2012 | 0 | 318837 | 0.00000000 |
| CAJAMARCA | 2011 | 0 | 711198 | 0.00000000 |
| CALLAO | 2011 | 0 | 480597 | 0.00000000 |
| CUSCO | 2011 | 0 | 610678 | 0.00000000 |
| HUANCAVELICA | 2011 | 0 | 216879 | 0.00000000 |
| HUANCAVELICA | 2014 | 0 | 201161 | 0.00000000 |
| HUANCAVELICA | 2019 | 0 | 184413 | 0.00000000 |
| HUANUCO | 2011 | 0 | 380794 | 0.00000000 |
| ICA | 2011 | 0 | 390469 | 0.00000000 |
| JUNIN | 2011 | 0 | 643407 | 0.00000000 |
| LA LIBERTAD | 2011 | 0 | 871946 | 0.00000000 |
| LAMBAYEQUE | 2011 | 0 | 599438 | 0.00000000 |
| LIMA | 2011 | 0 | 4638106 | 0.00000000 |
| LORETO | 2011 | 0 | 449948 | 0.00000000 |
| MADRE DE DIOS | 2011 | 0 | 54288 | 0.00000000 |
| MOQUEGUA | 2011 | 0 | 80126 | 0.00000000 |
| PASCO | 2011 | 0 | 135812 | 0.00000000 |
| PIURA | 2011 | 0 | 892571 | 0.00000000 |
| PUNO | 2011 | 0 | 639058 | 0.00000000 |
| PUNO | 2012 | 0 | 634504 | 0.00000000 |
| SAN MARTIN | 2019 | 0 | 417336 | 0.00000000 |
| TACNA | 2011 | 0 | 153747 | 0.00000000 |
| TUMBES | 2011 | 0 | 98207 | 0.00000000 |
| TUMBES | 2013 | 0 | 100943 | 0.00000000 |
| TUMBES | 2014 | 0 | 102532 | 0.00000000 |
| UCAYALI | 2011 | 0 | 226123 | 0.00000000 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_reactivos_allyears)TAMIZAJES POR AÑO
2022
data_total_reactivo22 <- data_total_reactivos %>%
filter(year == "2022")
ggplot() +
geom_sf(data = data_total_reactivo22, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2022",
fill = "Cases per 10000 hab")
data_total_reactivo22 <- data_total_reactivo22 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2022"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo22| Cases of syphilis in pregnants in 2022 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2022 | 44 | 80963 | 5.4345812 |
| UCAYALI | 2022 | 148 | 294437 | 5.0265422 |
| AMAZONAS | 2022 | 73 | 208363 | 3.5035011 |
| PASCO | 2022 | 39 | 130170 | 2.9960820 |
| HUANUCO | 2022 | 100 | 373411 | 2.6780143 |
| ICA | 2022 | 105 | 508445 | 2.0651201 |
| LORETO | 2022 | 103 | 505412 | 2.0379413 |
| CALLAO | 2022 | 122 | 602039 | 2.0264468 |
| JUNIN | 2022 | 137 | 689199 | 1.9878148 |
| TUMBES | 2022 | 22 | 119646 | 1.8387577 |
| SAN MARTIN | 2022 | 72 | 438293 | 1.6427367 |
| MOQUEGUA | 2022 | 15 | 91986 | 1.6306829 |
| LIMA | 2022 | 905 | 5698013 | 1.5882730 |
| AYACUCHO | 2022 | 44 | 326552 | 1.3474117 |
| APURIMAC | 2022 | 25 | 209664 | 1.1923840 |
| ANCASH | 2022 | 56 | 591791 | 0.9462800 |
| CUSCO | 2022 | 59 | 681600 | 0.8656103 |
| PIURA | 2022 | 89 | 1043380 | 0.8529970 |
| LA LIBERTAD | 2022 | 75 | 1047119 | 0.7162510 |
| HUANCAVELICA | 2022 | 12 | 173606 | 0.6912203 |
| LAMBAYEQUE | 2022 | 47 | 687215 | 0.6839199 |
| TACNA | 2022 | 12 | 188961 | 0.6350517 |
| AREQUIPA | 2022 | 48 | 789700 | 0.6078258 |
| CAJAMARCA | 2022 | 44 | 726838 | 0.6053619 |
| PUNO | 2022 | 16 | 620188 | 0.2579863 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo22)2021
data_total_reactivo21 <- data_total_reactivos %>%
filter(year == "2021")
ggplot() +
geom_sf(data = data_total_reactivo21, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2021",
fill = "Cases per 10000 hab")
data_total_reactivo21 <- data_total_reactivo21 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2021"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo21| Cases of syphilis in pregnants in 2021 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2021 | 43 | 78293 | 5.4921896 |
| UCAYALI | 2021 | 119 | 288087 | 4.1306966 |
| PASCO | 2021 | 44 | 131029 | 3.3580352 |
| HUANUCO | 2021 | 82 | 374996 | 2.1866900 |
| TACNA | 2021 | 37 | 185975 | 1.9895147 |
| CALLAO | 2021 | 113 | 591161 | 1.9114928 |
| ICA | 2021 | 87 | 497608 | 1.7483642 |
| AMAZONAS | 2021 | 36 | 207863 | 1.7319100 |
| LORETO | 2021 | 84 | 501365 | 1.6754261 |
| JUNIN | 2021 | 107 | 686597 | 1.5584105 |
| AYACUCHO | 2021 | 50 | 326668 | 1.5306060 |
| SAN MARTIN | 2021 | 65 | 432026 | 1.5045391 |
| MOQUEGUA | 2021 | 11 | 90971 | 1.2091766 |
| ANCASH | 2021 | 59 | 589223 | 1.0013187 |
| APURIMAC | 2021 | 19 | 210201 | 0.9038967 |
| LIMA | 2021 | 487 | 5606249 | 0.8686735 |
| PIURA | 2021 | 72 | 1030681 | 0.6985673 |
| TUMBES | 2021 | 7 | 117811 | 0.5941720 |
| CAJAMARCA | 2021 | 41 | 727255 | 0.5637637 |
| LAMBAYEQUE | 2021 | 37 | 680339 | 0.5438465 |
| CUSCO | 2021 | 34 | 676583 | 0.5025252 |
| LA LIBERTAD | 2021 | 47 | 1032621 | 0.4551525 |
| PUNO | 2021 | 25 | 623879 | 0.4007187 |
| HUANCAVELICA | 2021 | 7 | 177521 | 0.3943195 |
| AREQUIPA | 2021 | 30 | 776125 | 0.3865357 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo21)2020
data_total_reactivo20 <- data_total_reactivos %>%
filter(year == "2020")
ggplot() +
geom_sf(data = data_total_reactivo20, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2020",
fill = "Cases per 10000 hab")
data_total_reactivo20 <- data_total_reactivo20 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2020"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo20| Cases of syphilis in pregnants in 2020 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2020 | 21 | 75596 | 2.7779248 |
| PASCO | 2020 | 36 | 131652 | 2.7344818 |
| CALLAO | 2020 | 115 | 579808 | 1.9834152 |
| HUANUCO | 2020 | 74 | 375922 | 1.9684935 |
| LORETO | 2020 | 74 | 496559 | 1.4902559 |
| TACNA | 2020 | 24 | 182822 | 1.3127523 |
| ICA | 2020 | 63 | 486346 | 1.2953741 |
| AYACUCHO | 2020 | 35 | 326262 | 1.0727575 |
| MOQUEGUA | 2020 | 8 | 89885 | 0.8900261 |
| JUNIN | 2020 | 55 | 682973 | 0.8053027 |
| AMAZONAS | 2020 | 15 | 207005 | 0.7246202 |
| SAN MARTIN | 2020 | 30 | 425190 | 0.7055669 |
| APURIMAC | 2020 | 13 | 210366 | 0.6179706 |
| CUSCO | 2020 | 34 | 670532 | 0.5070601 |
| HUANCAVELICA | 2020 | 9 | 181196 | 0.4966997 |
| LIMA | 2020 | 264 | 5508910 | 0.4792237 |
| LAMBAYEQUE | 2020 | 32 | 672557 | 0.4757961 |
| TUMBES | 2020 | 5 | 115846 | 0.4316075 |
| CAJAMARCA | 2020 | 28 | 726446 | 0.3854381 |
| AREQUIPA | 2020 | 26 | 761731 | 0.3413278 |
| PIURA | 2020 | 31 | 1016979 | 0.3048244 |
| ANCASH | 2020 | 17 | 585806 | 0.2901985 |
| LA LIBERTAD | 2020 | 24 | 1016769 | 0.2360418 |
| PUNO | 2020 | 13 | 626381 | 0.2075414 |
| UCAYALI | 2020 | 5 | 281514 | 0.1776111 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo20)2019
data_total_reactivo19 <- data_total_reactivos %>%
filter(year == "2019")
ggplot() +
geom_sf(data = data_total_reactivo19, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2019",
fill = "Cases per 10000 hab")
data_total_reactivo19 <- data_total_reactivo19 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2019"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo19| Cases of syphilis in pregnants in 2019 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| PASCO | 2019 | 22 | 131887 | 1.66809466 |
| CALLAO | 2019 | 67 | 567532 | 1.18055017 |
| MADRE DE DIOS | 2019 | 4 | 72801 | 0.54944300 |
| LORETO | 2019 | 22 | 490451 | 0.44856673 |
| TUMBES | 2019 | 5 | 113640 | 0.43998592 |
| TACNA | 2019 | 6 | 179379 | 0.33448731 |
| HUANUCO | 2019 | 12 | 375743 | 0.31936723 |
| ICA | 2019 | 9 | 474202 | 0.18979254 |
| LAMBAYEQUE | 2019 | 12 | 663186 | 0.18094471 |
| PIURA | 2019 | 16 | 1001455 | 0.15976754 |
| LIMA | 2019 | 76 | 5401318 | 0.14070640 |
| MOQUEGUA | 2019 | 1 | 88666 | 0.11278280 |
| LA LIBERTAD | 2019 | 11 | 998509 | 0.11016425 |
| AMAZONAS | 2019 | 2 | 205550 | 0.09729993 |
| PUNO | 2019 | 6 | 626969 | 0.09569851 |
| AREQUIPA | 2019 | 5 | 745822 | 0.06704012 |
| CUSCO | 2019 | 4 | 662719 | 0.06035741 |
| APURIMAC | 2019 | 1 | 209909 | 0.04763969 |
| UCAYALI | 2019 | 1 | 274464 | 0.03643465 |
| AYACUCHO | 2019 | 1 | 324984 | 0.03077075 |
| CAJAMARCA | 2019 | 2 | 723592 | 0.02763989 |
| ANCASH | 2019 | 1 | 580954 | 0.01721307 |
| JUNIN | 2019 | 1 | 677635 | 0.01475721 |
| HUANCAVELICA | 2019 | 0 | 184413 | 0.00000000 |
| SAN MARTIN | 2019 | 0 | 417336 | 0.00000000 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo19)2018
data_total_reactivo18 <- data_total_reactivos %>%
filter(year == "2018")
ggplot() +
geom_sf(data = data_total_reactivo18, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2018",
fill = "Cases per 10000 hab")
data_total_reactivo18 <- data_total_reactivo18 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2018"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo18| Cases of syphilis in pregnants in 2018 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| MADRE DE DIOS | 2018 | 51 | 69926 | 7.2934245 |
| AMAZONAS | 2018 | 121 | 203569 | 5.9439306 |
| PASCO | 2018 | 72 | 131785 | 5.4634442 |
| UCAYALI | 2018 | 132 | 266990 | 4.9440054 |
| SAN MARTIN | 2018 | 121 | 408581 | 2.9614691 |
| LORETO | 2018 | 138 | 483188 | 2.8560312 |
| HUANUCO | 2018 | 95 | 374602 | 2.5360249 |
| JUNIN | 2018 | 169 | 670777 | 2.5194662 |
| TACNA | 2018 | 43 | 175677 | 2.4476739 |
| CUSCO | 2018 | 143 | 653351 | 2.1887163 |
| CALLAO | 2018 | 103 | 554432 | 1.8577571 |
| PUNO | 2018 | 113 | 625906 | 1.8053829 |
| TUMBES | 2018 | 16 | 111218 | 1.4386161 |
| LA LIBERTAD | 2018 | 136 | 978126 | 1.3904139 |
| ICA | 2018 | 61 | 461287 | 1.3223871 |
| HUANCAVELICA | 2018 | 20 | 187245 | 1.0681193 |
| PIURA | 2018 | 100 | 984282 | 1.0159690 |
| LAMBAYEQUE | 2018 | 63 | 652399 | 0.9656667 |
| LIMA | 2018 | 494 | 5284576 | 0.9347959 |
| ANCASH | 2018 | 47 | 574828 | 0.8176359 |
| APURIMAC | 2018 | 16 | 208910 | 0.7658800 |
| AYACUCHO | 2018 | 24 | 322938 | 0.7431767 |
| MOQUEGUA | 2018 | 4 | 87325 | 0.4580590 |
| CAJAMARCA | 2018 | 31 | 718945 | 0.4311874 |
| AREQUIPA | 2018 | 21 | 728576 | 0.2882335 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo18)2017
data_total_reactivo17 <- data_total_reactivos %>%
filter(year == "2017")
ggplot() +
geom_sf(data = data_total_reactivo17, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2017",
fill = "Cases per 10000 hab")
data_total_reactivo17 <- data_total_reactivo17 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2017"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo17| Cases of syphilis in pregnants in 2017 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2017 | 105 | 201423 | 5.2129101 |
| MADRE DE DIOS | 2017 | 34 | 67082 | 5.0684237 |
| PASCO | 2017 | 66 | 131573 | 5.0162267 |
| UCAYALI | 2017 | 88 | 259475 | 3.3914635 |
| LORETO | 2017 | 141 | 475588 | 2.9647510 |
| HUANUCO | 2017 | 98 | 373161 | 2.6262123 |
| SAN MARTIN | 2017 | 74 | 399596 | 1.8518704 |
| CALLAO | 2017 | 80 | 541179 | 1.4782540 |
| CUSCO | 2017 | 91 | 643519 | 1.4140997 |
| JUNIN | 2017 | 89 | 663430 | 1.3415130 |
| PUNO | 2017 | 68 | 624277 | 1.0892601 |
| HUANCAVELICA | 2017 | 19 | 190016 | 0.9999158 |
| TACNA | 2017 | 15 | 171904 | 0.8725800 |
| LA LIBERTAD | 2017 | 81 | 957196 | 0.8462217 |
| TUMBES | 2017 | 9 | 108747 | 0.8276090 |
| ICA | 2017 | 35 | 448283 | 0.7807568 |
| AYACUCHO | 2017 | 22 | 320653 | 0.6860999 |
| LIMA | 2017 | 354 | 5165717 | 0.6852873 |
| PIURA | 2017 | 63 | 966689 | 0.6517091 |
| MOQUEGUA | 2017 | 5 | 85953 | 0.5817133 |
| LAMBAYEQUE | 2017 | 36 | 641219 | 0.5614307 |
| APURIMAC | 2017 | 11 | 207742 | 0.5295029 |
| ANCASH | 2017 | 30 | 568305 | 0.5278856 |
| AREQUIPA | 2017 | 23 | 711041 | 0.3234694 |
| CAJAMARCA | 2017 | 22 | 713729 | 0.3082402 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo17)2016
data_total_reactivo16 <- data_total_reactivos %>%
filter(year == "2016")
ggplot() +
geom_sf(data = data_total_reactivo16, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2016",
fill = "Cases per 10000 hab")
data_total_reactivo16 <- data_total_reactivo16 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2016"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo16| Cases of syphilis in pregnants in 2016 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2016 | 149 | 199474 | 7.4696452 |
| MADRE DE DIOS | 2016 | 20 | 64381 | 3.1065066 |
| UCAYALI | 2016 | 76 | 252303 | 3.0122511 |
| PASCO | 2016 | 39 | 131485 | 2.9661178 |
| LORETO | 2016 | 121 | 468466 | 2.5828982 |
| HUANUCO | 2016 | 94 | 372089 | 2.5262773 |
| SAN MARTIN | 2016 | 77 | 391051 | 1.9690526 |
| CUSCO | 2016 | 72 | 634312 | 1.1350881 |
| CALLAO | 2016 | 58 | 528445 | 1.0975598 |
| MOQUEGUA | 2016 | 9 | 84642 | 1.0633019 |
| LAMBAYEQUE | 2016 | 62 | 630665 | 0.9830893 |
| JUNIN | 2016 | 64 | 656630 | 0.9746737 |
| TUMBES | 2016 | 10 | 106390 | 0.9399380 |
| LA LIBERTAD | 2016 | 73 | 937293 | 0.7788386 |
| TACNA | 2016 | 13 | 168246 | 0.7726781 |
| LIMA | 2016 | 363 | 5051764 | 0.7185609 |
| PIURA | 2016 | 64 | 949910 | 0.6737480 |
| AYACUCHO | 2016 | 20 | 318655 | 0.6276380 |
| HUANCAVELICA | 2016 | 12 | 193049 | 0.6216038 |
| PUNO | 2016 | 37 | 623167 | 0.5937413 |
| ANCASH | 2016 | 33 | 562270 | 0.5869066 |
| ICA | 2016 | 24 | 435873 | 0.5506191 |
| APURIMAC | 2016 | 8 | 206775 | 0.3868940 |
| AREQUIPA | 2016 | 18 | 694265 | 0.2592670 |
| CAJAMARCA | 2016 | 16 | 709170 | 0.2256159 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo16)2015
data_total_reactivo15 <- data_total_reactivos %>%
filter(year == "2015")
ggplot() +
geom_sf(data = data_total_reactivo15, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2015",
fill = "Cases per 10000 hab")
data_total_reactivo15 <- data_total_reactivo15 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2015"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo15| Cases of syphilis in pregnants in 2015 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2015 | 136 | 198081 | 6.8658781 |
| HUANUCO | 2015 | 102 | 372053 | 2.7415449 |
| SAN MARTIN | 2015 | 96 | 383619 | 2.5024829 |
| PASCO | 2015 | 27 | 131747 | 2.0493825 |
| MADRE DE DIOS | 2015 | 12 | 61928 | 1.9377341 |
| UCAYALI | 2015 | 47 | 245857 | 1.9116804 |
| JUNIN | 2015 | 92 | 651410 | 1.4123210 |
| LORETO | 2015 | 56 | 462639 | 1.2104470 |
| LAMBAYEQUE | 2015 | 62 | 621762 | 0.9971661 |
| TACNA | 2015 | 16 | 164889 | 0.9703498 |
| LA LIBERTAD | 2015 | 75 | 919993 | 0.8152236 |
| CUSCO | 2015 | 48 | 626826 | 0.7657627 |
| MOQUEGUA | 2015 | 6 | 83488 | 0.7186662 |
| AYACUCHO | 2015 | 19 | 317473 | 0.5984761 |
| PIURA | 2015 | 50 | 935175 | 0.5346593 |
| TUMBES | 2015 | 5 | 104311 | 0.4793358 |
| ANCASH | 2015 | 26 | 557603 | 0.4662816 |
| LIMA | 2015 | 188 | 4949734 | 0.3798184 |
| APURIMAC | 2015 | 6 | 206383 | 0.2907216 |
| CAJAMARCA | 2015 | 17 | 706496 | 0.2406242 |
| PUNO | 2015 | 15 | 623663 | 0.2405145 |
| AREQUIPA | 2015 | 15 | 679297 | 0.2208165 |
| CALLAO | 2015 | 11 | 516902 | 0.2128063 |
| HUANCAVELICA | 2015 | 4 | 196670 | 0.2033864 |
| ICA | 2015 | 8 | 424740 | 0.1883505 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo15)2014
data_total_reactivo14 <- data_total_reactivos %>%
filter(year == "2014")
ggplot() +
geom_sf(data = data_total_reactivo14, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2014",
fill = "Cases per 10000 hab")
data_total_reactivo14 <- data_total_reactivo14 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2014"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo14| Cases of syphilis in pregnants in 2014 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2014 | 111 | 197364 | 5.6241260 |
| MADRE DE DIOS | 2014 | 16 | 59752 | 2.6777346 |
| HUANUCO | 2014 | 89 | 373348 | 2.3838349 |
| PASCO | 2014 | 30 | 132464 | 2.2647663 |
| SAN MARTIN | 2014 | 58 | 377465 | 1.5365663 |
| UCAYALI | 2014 | 30 | 240219 | 1.2488604 |
| MOQUEGUA | 2014 | 8 | 82507 | 0.9696147 |
| JUNIN | 2014 | 51 | 648069 | 0.7869532 |
| LORETO | 2014 | 33 | 458340 | 0.7199895 |
| AYACUCHO | 2014 | 21 | 317345 | 0.6617404 |
| ICA | 2014 | 25 | 414943 | 0.6024924 |
| LA LIBERTAD | 2014 | 46 | 905654 | 0.5079202 |
| CUSCO | 2014 | 28 | 621301 | 0.4506672 |
| CAJAMARCA | 2014 | 31 | 706065 | 0.4390531 |
| TACNA | 2014 | 7 | 161839 | 0.4325286 |
| PIURA | 2014 | 33 | 922689 | 0.3576503 |
| APURIMAC | 2014 | 7 | 206684 | 0.3386813 |
| LIMA | 2014 | 159 | 4860982 | 0.3270944 |
| ANCASH | 2014 | 16 | 554473 | 0.2885623 |
| PUNO | 2014 | 17 | 626183 | 0.2714861 |
| AREQUIPA | 2014 | 18 | 666305 | 0.2701466 |
| LAMBAYEQUE | 2014 | 16 | 614728 | 0.2602777 |
| CALLAO | 2014 | 7 | 506714 | 0.1381450 |
| HUANCAVELICA | 2014 | 0 | 201161 | 0.0000000 |
| TUMBES | 2014 | 0 | 102532 | 0.0000000 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo14)2013
data_total_reactivo13 <- data_total_reactivos %>%
filter(year == "2013")
ggplot() +
geom_sf(data = data_total_reactivo13, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2013",
fill = "Cases per 10000 hab")
data_total_reactivo13 <- data_total_reactivo13 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2013"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo13| Cases of syphilis in pregnants in 2013 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2013 | 144 | 197083 | 7.30656627 |
| MADRE DE DIOS | 2013 | 16 | 57780 | 2.76912426 |
| HUANUCO | 2013 | 98 | 375531 | 2.60963809 |
| PASCO | 2013 | 30 | 133485 | 2.24744353 |
| JUNIN | 2013 | 86 | 645918 | 1.33143836 |
| SAN MARTIN | 2013 | 41 | 372145 | 1.10172110 |
| LA LIBERTAD | 2013 | 65 | 893226 | 0.72769937 |
| CUSCO | 2013 | 38 | 617011 | 0.61587233 |
| LORETO | 2013 | 28 | 455024 | 0.61535216 |
| MOQUEGUA | 2013 | 5 | 81638 | 0.61245988 |
| PIURA | 2013 | 36 | 911633 | 0.39489575 |
| TACNA | 2013 | 5 | 158971 | 0.31452277 |
| ICA | 2013 | 11 | 406028 | 0.27091728 |
| LIMA | 2013 | 122 | 4780815 | 0.25518662 |
| ANCASH | 2013 | 13 | 552295 | 0.23538145 |
| AREQUIPA | 2013 | 13 | 654590 | 0.19859760 |
| LAMBAYEQUE | 2013 | 12 | 608884 | 0.19708187 |
| UCAYALI | 2013 | 4 | 235131 | 0.17011793 |
| HUANCAVELICA | 2013 | 3 | 206304 | 0.14541647 |
| CALLAO | 2013 | 7 | 497432 | 0.14072275 |
| PUNO | 2013 | 8 | 630004 | 0.12698332 |
| AYACUCHO | 2013 | 4 | 317917 | 0.12581900 |
| APURIMAC | 2013 | 2 | 207430 | 0.09641807 |
| CAJAMARCA | 2013 | 6 | 707062 | 0.08485819 |
| TUMBES | 2013 | 0 | 100943 | 0.00000000 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo13)2012
data_total_reactivo12 <- data_total_reactivos %>%
filter(year == "2012")
ggplot() +
geom_sf(data = data_total_reactivo12, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2012",
fill = "Cases per 10000 hab")
data_total_reactivo12 <- data_total_reactivo12 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2012"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo12| Cases of syphilis in pregnants in 2012 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2012 | 46 | 197058 | 2.33433811 |
| MADRE DE DIOS | 2012 | 10 | 55971 | 1.78663951 |
| SAN MARTIN | 2012 | 19 | 367403 | 0.51714330 |
| TACNA | 2012 | 7 | 156276 | 0.44792547 |
| MOQUEGUA | 2012 | 3 | 80853 | 0.37104375 |
| JUNIN | 2012 | 20 | 644512 | 0.31031230 |
| LA LIBERTAD | 2012 | 18 | 882171 | 0.20404207 |
| HUANCAVELICA | 2012 | 4 | 211683 | 0.18896180 |
| HUANUCO | 2012 | 7 | 378160 | 0.18510683 |
| PASCO | 2012 | 2 | 134653 | 0.14852993 |
| TUMBES | 2012 | 1 | 99513 | 0.10048938 |
| LAMBAYEQUE | 2012 | 6 | 603897 | 0.09935469 |
| CAJAMARCA | 2012 | 7 | 708952 | 0.09873729 |
| AREQUIPA | 2012 | 5 | 643896 | 0.07765229 |
| ICA | 2012 | 3 | 397901 | 0.07539564 |
| LIMA | 2012 | 34 | 4707203 | 0.07222973 |
| CUSCO | 2012 | 4 | 613591 | 0.06519000 |
| PIURA | 2012 | 5 | 901697 | 0.05545100 |
| LORETO | 2012 | 2 | 452343 | 0.04421424 |
| UCAYALI | 2012 | 1 | 230473 | 0.04338903 |
| ANCASH | 2012 | 2 | 550814 | 0.03630990 |
| CALLAO | 2012 | 1 | 488810 | 0.02045785 |
| APURIMAC | 2012 | 0 | 208444 | 0.00000000 |
| AYACUCHO | 2012 | 0 | 318837 | 0.00000000 |
| PUNO | 2012 | 0 | 634504 | 0.00000000 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo12)2011
data_total_reactivo11 <- data_total_reactivos %>%
filter(year == "2011")
ggplot() +
geom_sf(data = data_total_reactivo11, aes(geometry = geometry,fill = rate), color = "white", size = 0.2) +
scale_fill_gradient(low = "darkgoldenrod1", high = "darkred") +
labs(title="Syphilis cases in pregnants, 2011",
fill = "Cases per 10000 hab")
data_total_reactivo11 <- data_total_reactivo11 %>%
select(NOMBDEP, year, tamizaje_reactivo, POBFEM, rate) %>%
arrange(desc(rate)) %>%
gt(groupname_col = FALSE) %>%
tab_header(
title = "Cases of syphilis in pregnants in 2011"
) %>%
tab_footnote(
footnote = "Population at June 30th",
locations = cells_column_labels(columns = POBFEM)
) %>%
tab_footnote(
footnote = "Rate per 10 000 hab.",
locations = cells_column_labels(columns = rate)
)
data_total_reactivo11| Cases of syphilis in pregnants in 2011 | ||||
| NOMBDEP | year | tamizaje_reactivo | POBFEM1 | rate2 |
|---|---|---|---|---|
| AMAZONAS | 2011 | 17 | 197113 | 0.86244946 |
| SAN MARTIN | 2011 | 1 | 362987 | 0.02754920 |
| AREQUIPA | 2011 | 1 | 633964 | 0.01577377 |
| ANCASH | 2011 | 0 | 549780 | 0.00000000 |
| APURIMAC | 2011 | 0 | 209550 | 0.00000000 |
| AYACUCHO | 2011 | 0 | 319750 | 0.00000000 |
| CAJAMARCA | 2011 | 0 | 711198 | 0.00000000 |
| CALLAO | 2011 | 0 | 480597 | 0.00000000 |
| CUSCO | 2011 | 0 | 610678 | 0.00000000 |
| HUANCAVELICA | 2011 | 0 | 216879 | 0.00000000 |
| HUANUCO | 2011 | 0 | 380794 | 0.00000000 |
| ICA | 2011 | 0 | 390469 | 0.00000000 |
| JUNIN | 2011 | 0 | 643407 | 0.00000000 |
| LA LIBERTAD | 2011 | 0 | 871946 | 0.00000000 |
| LAMBAYEQUE | 2011 | 0 | 599438 | 0.00000000 |
| LIMA | 2011 | 0 | 4638106 | 0.00000000 |
| LORETO | 2011 | 0 | 449948 | 0.00000000 |
| MADRE DE DIOS | 2011 | 0 | 54288 | 0.00000000 |
| MOQUEGUA | 2011 | 0 | 80126 | 0.00000000 |
| PASCO | 2011 | 0 | 135812 | 0.00000000 |
| PIURA | 2011 | 0 | 892571 | 0.00000000 |
| PUNO | 2011 | 0 | 639058 | 0.00000000 |
| TACNA | 2011 | 0 | 153747 | 0.00000000 |
| TUMBES | 2011 | 0 | 98207 | 0.00000000 |
| UCAYALI | 2011 | 0 | 226123 | 0.00000000 |
| 1 Population at June 30th | ||||
| 2 Rate per 10 000 hab. | ||||
rm(data_total_reactivo11)